{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "learning-human-strokes.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [
        "9P5f_uzAq_5g"
      ],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python2",
      "display_name": "Python 2"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/reiinakano/neural-painters/blob/master/notebooks/learning_human_strokes.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "nln5BXR-3B5e",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Optionally connect to Google Drive"
      ]
    },
    {
      "metadata": {
        "id": "ESVcsBip1IIZ",
        "colab_type": "code",
        "outputId": "627b8d39-eb8b-4004-f75f-2e42cc93bbd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 127
        }
      },
      "cell_type": "code",
      "source": [
        "#from google.colab import drive\n",
        "#drive.mount('/drive')"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3Aietf%3Awg%3Aoauth%3A2.0%3Aoob&scope=email%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdocs.test%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fdrive.photos.readonly%20https%3A%2F%2Fwww.googleapis.com%2Fauth%2Fpeopleapi.readonly&response_type=code\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "9P5f_uzAq_5g",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Install MyPaint"
      ]
    },
    {
      "metadata": {
        "id": "bAWyePtkn3av",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Install mypaint\n",
        "!apt-get update\n",
        "!apt-get install libjson-c-dev libgirepository1.0-dev libglib2.0-dev\n",
        "!apt-get install autotools-dev intltool gettext libtool\n",
        "!apt-get install swig python-setuptools gettext g++\n",
        "!apt-get install -y libgtk-3-dev python-gi-dev\n",
        "!apt-get install -y libpng-dev liblcms2-dev libjson-c-dev\n",
        "!apt-get install -y gir1.2-gtk-3.0 python-gi-cairo\n",
        "!apt-get install scons\n",
        "\n",
        "!wget https://github.com/mypaint/libmypaint/releases/download/v1.3.0/libmypaint-1.3.0.tar.xz\n",
        "!tar -xvf libmypaint-1.3.0.tar.xz\n",
        "!mv libmypaint-1.3.0 libmypaint\n",
        "\n",
        "!cd libmypaint && ./configure && make install\n",
        "\n",
        "!wget https://github.com/mypaint/mypaint/releases/download/v1.2.1/mypaint-1.2.1.tar.xz\n",
        "!tar -xvf mypaint-1.2.1.tar.xz\n",
        "!mv mypaint-1.2.1 mypaint\n",
        "!cd mypaint && scons && scons install\n",
        "\n",
        "!ldconfig\n",
        "\n",
        "!pip install ipdb tqdm pathlib cloudpickle matplotlib"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nx2C15rB4Huz",
        "colab_type": "code",
        "outputId": "85c24cf4-40b7-4847-8b40-3f095a9feed7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        }
      },
      "cell_type": "code",
      "source": [
        "!pip install future-fstrings"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting future-fstrings\n",
            "  Downloading https://files.pythonhosted.org/packages/d5/10/de62670513b7b2e7de32bbabb662cbc05ccee49fadf6f69725388df780e8/future_fstrings-1.0.0-py2.py3-none-any.whl\n",
            "Collecting tokenize-rt; python_version < \"3.6\" (from future-fstrings)\n",
            "  Downloading https://files.pythonhosted.org/packages/43/4b/c5df89ff5b38afffc04fb208c9b1fce30c1426788a368d7039b4cbcf524e/tokenize_rt-2.2.0-py2.py3-none-any.whl\n",
            "Installing collected packages: tokenize-rt, future-fstrings\n",
            "Successfully installed future-fstrings-1.0.0 tokenize-rt-2.2.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "1ueV31ATgbQp",
        "colab_type": "code",
        "outputId": "e6cbf983-8ce6-46cb-ccef-4a2484a6515c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 197
        }
      },
      "cell_type": "code",
      "source": [
        "!git clone https://github.com/reiinakano/SPIRAL-tensorflow.git\n",
        "!cd SPIRAL-tensorflow && git checkout reiinakano-patch-2  #reiinakano-patches\n",
        "!cd SPIRAL-tensorflow && git pull"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'SPIRAL-tensorflow'...\n",
            "remote: Enumerating objects: 8, done.\u001b[K\n",
            "remote: Counting objects: 100% (8/8), done.\u001b[K\n",
            "remote: Compressing objects: 100% (8/8), done.\u001b[K\n",
            "remote: Total 154 (delta 3), reused 0 (delta 0), pack-reused 146\u001b[K\n",
            "Receiving objects: 100% (154/154), 1.36 MiB | 3.49 MiB/s, done.\n",
            "Resolving deltas: 100% (75/75), done.\n",
            "Branch 'reiinakano-patch-2' set up to track remote branch 'reiinakano-patch-2' from 'origin'.\n",
            "Switched to a new branch 'reiinakano-patch-2'\n",
            "Already up to date.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "a2NYZTrugOeu",
        "colab_type": "code",
        "outputId": "3bd8cdd2-832f-4f68-ff1c-bbc24f33b96c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 251
        }
      },
      "cell_type": "code",
      "source": [
        "! wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
        "! unzip ngrok-stable-linux-amd64.zip"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "--2019-04-13 13:25:36--  https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip\n",
            "Resolving bin.equinox.io (bin.equinox.io)... 54.173.32.212, 34.206.9.96, 35.173.6.94, ...\n",
            "Connecting to bin.equinox.io (bin.equinox.io)|54.173.32.212|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 14977695 (14M) [application/octet-stream]\n",
            "Saving to: ‘ngrok-stable-linux-amd64.zip’\n",
            "\n",
            "ngrok-stable-linux- 100%[===================>]  14.28M  14.4MB/s    in 1.0s    \n",
            "\n",
            "2019-04-13 13:25:38 (14.4 MB/s) - ‘ngrok-stable-linux-amd64.zip’ saved [14977695/14977695]\n",
            "\n",
            "Archive:  ngrok-stable-linux-amd64.zip\n",
            "  inflating: ngrok                   \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "_YFJNtlKvj4p",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "wRKxCFkx3Sfe",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Download pre-trained VAE and GAN Neural Painters\n",
        "\n",
        "I have prepared pre-trained VAE and GAN Neural Painters here but feel free to use your own if you wish.\n",
        "\n",
        "Place the VAE in tf_vae and the GAN in tf_gan3"
      ]
    },
    {
      "metadata": {
        "id": "Bgx8N4o6uXJE",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "!mkdir tf_vae\n",
        "!wget -O tf_vae/vae-300000.index 'https://docs.google.com/uc?export=download&id=1ulHdDxebH46m_0ZoLa2Wsz_6vStYqJQm'\n",
        "!wget -O tf_vae/vae-300000.meta 'https://docs.google.com/uc?export=download&id=1nHN_i7Ro9g0lP4y_YQCvIWrOVX1I3CJa'\n",
        "!wget -O tf_vae/vae-300000.data-00000-of-00001 'https://docs.google.com/uc?export=download&id=18rAJcUJwFJOAcjzsabtqK12udsHMZkVk'\n",
        "!wget -O tf_vae/checkpoint 'https://docs.google.com/uc?export=download&id=18U4qMNBdyvEk-Y-Mr3MNPEHSHxhcO9hn'\n",
        "\n",
        "!mkdir tf_gan3\n",
        "!wget -O tf_gan3/gan-571445.meta 'https://docs.google.com/uc?export=download&id=15kEG1Tiu2FUg5SILVt_9yOsSd3QHwVGA'\n",
        "!wget -O tf_gan3/gan-571445.index 'https://docs.google.com/uc?export=download&id=11uyFbQsRZoWa9Yq52AFXDXPjPQoGF_ER'\n",
        "!wget -O tf_gan3/gan-571445.data-00000-of-00001 'https://docs.google.com/uc?export=download&id=11cbvz-CH3KvfZEwNQ2OUujfbf6AKNoQa'\n",
        "!wget -O tf_gan3/checkpoint 'https://docs.google.com/uc?export=download&id=1A539u51t0L31Ab1M2uPUV2SsCFsNDQRo'\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "cf8wNMfICWqW",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Imports\n",
        "\n",
        "If you encounter `SyntaxError: encoding problem: future_fstrings` then please \"Restart Runtime\" (NOT \"Reset all Runtimes\") and start from this point. I think this is because we are installing a library \"future-fstrings\" but it won't get loaded in until the runtime is reset :( If someone knows a fix please let me know."
      ]
    },
    {
      "metadata": {
        "id": "QYBRFuZqRqHL",
        "colab_type": "code",
        "outputId": "cf7d5bfd-7155-4e13-82c7-203654aa42aa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from pathlib import Path\n",
        "import json\n",
        "\n",
        "import tensorflow as tf\n",
        "import os\n",
        "import random\n",
        "import time\n",
        "from collections import namedtuple\n",
        "\n",
        "\n",
        "import sys\n",
        "sys.path.append('mypaint')\n",
        "sys.path.append('SPIRAL-tensorflow')\n",
        "\n",
        "from tqdm import tqdm\n",
        "import tensorflow as tf\n",
        "from PIL import Image, ImageDraw\n",
        "from collections import defaultdict\n",
        "\n",
        "from lib import surface, tiledsurface, brush\n",
        "import utils as ut\n",
        "from envs.mypaint_utils import *\n",
        "\n",
        "import moviepy.editor as mpy\n",
        "from IPython.display import display\n",
        "\n",
        "import tensorflow.contrib.layers as tcl\n",
        "\n",
        "import imageio\n",
        "\n",
        "\n",
        "print(tf.__version__)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "YxJPk3OoDQOx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "imageio.plugins.freeimage.download()"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EbQ6yU1vEHZN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class args:\n",
        "  jump=True\n",
        "  curve=True\n",
        "  screen_size=64\n",
        "  location_size=32\n",
        "  color_channel=3\n",
        "  brush_path='SPIRAL-tensorflow/assets/brushes/dry_brush.myb'\n",
        "  train=True\n",
        "  data_dir=Path('data')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ivFxNXdqC7G9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# paint environment"
      ]
    },
    {
      "metadata": {
        "id": "vqG26OYpDHXe",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class PaintMode:\n",
        "  STROKES_ONLY = 0\n",
        "  JUMP_STROKES = 1\n",
        "  CONNECTED_STROKES = 2\n",
        "\n",
        "class ColorEnv():\n",
        "    head = 0.25\n",
        "    tail = 0.75\n",
        "    \n",
        "    # all 0 to 1\n",
        "    actions_to_idx = {\n",
        "        'pressure': 0,\n",
        "        'size': 1,\n",
        "        'control_x': 2,\n",
        "        'control_y': 3,\n",
        "        'end_x': 4,\n",
        "        'end_y': 5,\n",
        "        'color_r': 6,\n",
        "        'color_g': 7,\n",
        "        'color_b': 8,\n",
        "        'start_x': 9,\n",
        "        'start_y': 10,\n",
        "        'entry_pressure': 11,\n",
        "    }\n",
        "\n",
        "    def __init__(self, args, paint_mode=PaintMode.JUMP_STROKES):\n",
        "        self.args = args\n",
        "        self.paint_mode = paint_mode\n",
        "\n",
        "        # screen\n",
        "        self.screen_size = args.screen_size\n",
        "        self.height, self.width = self.screen_size, self.screen_size\n",
        "        self.observation_shape = [\n",
        "                self.height, self.width, args.color_channel]\n",
        "\n",
        "        # location\n",
        "        self.location_size = args.location_size\n",
        "        self.location_shape = [self.location_size, self.location_size]\n",
        "        \n",
        "        self.prev_x, self.prev_y, self.prev_pressure = None, None, None\n",
        "    \n",
        "    @staticmethod\n",
        "    def pretty_print_action(ac):\n",
        "        for k, v in ColorEnv.actions_to_idx.items():\n",
        "            print(k, ac[v])\n",
        "    \n",
        "    def random_action(self):\n",
        "        return np.random.uniform(size=[len(self.actions_to_idx)])\n",
        "      \n",
        "    def reset(self):\n",
        "        self.intermediate_images = []\n",
        "        self.prev_x, self.prev_y, self.prev_pressure = None, None, None\n",
        "\n",
        "        self.s = tiledsurface.Surface()\n",
        "        self.s.flood_fill(0, 0, (255, 255, 255), (0, 0, 64, 64), 0, self.s)\n",
        "        self.s.begin_atomic()\n",
        "\n",
        "        with open(self.args.brush_path) as fp:\n",
        "            self.bi = brush.BrushInfo(fp.read())\n",
        "        self.b = brush.Brush(self.bi)\n",
        "\n",
        "    def draw(self, ac, s=None, dtime=1):\n",
        "        # Just added this\n",
        "        if self.paint_mode == PaintMode.STROKES_ONLY:\n",
        "          self.s.clear()\n",
        "          self.s.flood_fill(0, 0, (255, 255, 255), (0, 0, 64, 64), 0, self.s)\n",
        "          self.s.end_atomic()\n",
        "          self.s.begin_atomic()\n",
        "        \n",
        "        if s is None:\n",
        "            s = self.s\n",
        "\n",
        "        s_x, s_y = ac[self.actions_to_idx['start_x']]*64, ac[self.actions_to_idx['start_y']]*64  \n",
        "        e_x, e_y = ac[self.actions_to_idx['end_x']]*64, ac[self.actions_to_idx['end_y']]*64\n",
        "        c_x, c_y = ac[self.actions_to_idx['control_x']]*64, ac[self.actions_to_idx['control_y']]*64\n",
        "        color = (\n",
        "            ac[self.actions_to_idx['color_r']],\n",
        "            ac[self.actions_to_idx['color_g']],\n",
        "            ac[self.actions_to_idx['color_b']],\n",
        "        )\n",
        "        pressure = ac[self.actions_to_idx['pressure']]*0.8\n",
        "        entry_pressure = ac[self.actions_to_idx['entry_pressure']]*0.8\n",
        "        size = ac[self.actions_to_idx['size']] * 2.\n",
        "        \n",
        "        if self.paint_mode == PaintMode.CONNECTED_STROKES:\n",
        "            if self.prev_x is not None:\n",
        "                s_x, s_y, entry_pressure = self.prev_x, self.prev_y, self.prev_pressure\n",
        "            self.prev_x, self.prev_y, self.prev_pressure = e_x, e_y, pressure\n",
        "\n",
        "        self.b.brushinfo.set_color_rgb(color)\n",
        "        \n",
        "        self.b.brushinfo.set_base_value('radius_logarithmic', size)\n",
        "\n",
        "        # Move brush to starting point without leaving it on the canvas.\n",
        "        self._stroke_to(s_x, s_y, 0)\n",
        "\n",
        "        self._draw(s_x, s_y, e_x, e_y, c_x, c_y, entry_pressure, pressure, size, color, dtime)\n",
        "\n",
        "    def _draw(self, s_x, s_y, e_x, e_y, c_x, c_y,\n",
        "              entry_pressure, pressure, size, color, dtime):\n",
        "\n",
        "        # if straight line or jump\n",
        "        if pressure == 0:\n",
        "            self.b.stroke_to(\n",
        "                    self.s.backend, e_x, e_y, pressure, 0, 0, dtime)\n",
        "        else:\n",
        "            self.curve(c_x, c_y, s_x, s_y, e_x, e_y, entry_pressure, pressure)\n",
        "            \n",
        "        # Relieve brush pressure for next jump\n",
        "        self._stroke_to(e_x, e_y, 0)\n",
        "\n",
        "        self.s.end_atomic()\n",
        "        self.s.begin_atomic()\n",
        "\n",
        "    # sx, sy = starting point\n",
        "    # ex, ey = end point\n",
        "    # kx, ky = curve point from last line\n",
        "    # lx, ly = last point from InteractionMode update\n",
        "    def curve(self, cx, cy, sx, sy, ex, ey, entry_pressure, pressure):\n",
        "        #entry_p, midpoint_p, junk, prange2, head, tail\n",
        "        entry_p, midpoint_p, prange1, prange2, h, t = \\\n",
        "                self._line_settings(entry_pressure, pressure)\n",
        "\n",
        "        points_in_curve = 100\n",
        "        mx, my = midpoint(sx, sy, ex, ey)\n",
        "        length, nx, ny = length_and_normal(mx, my, cx, cy)\n",
        "        cx, cy = multiply_add(mx, my, nx, ny, length*2)\n",
        "        x1, y1 = difference(sx, sy, cx, cy)\n",
        "        x2, y2 = difference(cx, cy, ex, ey)\n",
        "        head = points_in_curve * h\n",
        "        head_range = int(head)+1\n",
        "        tail = points_in_curve * t\n",
        "        tail_range = int(tail)+1\n",
        "        tail_length = points_in_curve - tail\n",
        "\n",
        "        # Beginning\n",
        "        px, py = point_on_curve_1(1, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "        length, nx, ny = length_and_normal(sx, sy, px, py)\n",
        "        bx, by = multiply_add(sx, sy, nx, ny, 0.25)\n",
        "        self._stroke_to(bx, by, entry_p)\n",
        "        pressure = abs(1/head * prange1 + entry_p)\n",
        "        self._stroke_to(px, py, pressure)\n",
        "\n",
        "        for i in xrange(2, head_range):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            pressure = abs(i/head * prange1 + entry_p)\n",
        "            self._stroke_to(px, py, pressure)\n",
        "\n",
        "        # Middle\n",
        "        for i in xrange(head_range, tail_range):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            self._stroke_to(px, py, midpoint_p)\n",
        "\n",
        "        # End\n",
        "        for i in xrange(tail_range, points_in_curve+1):\n",
        "            px, py = point_on_curve_1(i, cx, cy, sx, sy, x1, y1, x2, y2)\n",
        "            pressure = abs((i-tail)/tail_length * prange2 + midpoint_p)\n",
        "            self._stroke_to(px, py, pressure)\n",
        "\n",
        "        return pressure\n",
        "\n",
        "    def _stroke_to(self, x, y, pressure, duration=0.1):\n",
        "        self.b.stroke_to(\n",
        "                self.s.backend,\n",
        "                x, y,\n",
        "                pressure,\n",
        "                0.0, 0.0,\n",
        "                duration)\n",
        "        self.s.end_atomic()\n",
        "        self.s.begin_atomic()\n",
        "        self.intermediate_images.append(self.image)\n",
        "\n",
        "    def save_image(self, path=\"test.png\"):\n",
        "        Image.fromarray(self.image.astype(np.uint8).squeeze()).save(path)\n",
        "        #self.s.save_as_png(path, alpha=False)\n",
        "\n",
        "    @property\n",
        "    def image(self):\n",
        "        rect = [0, 0, self.height, self.width]\n",
        "        scanline_strips = \\\n",
        "                surface.scanline_strips_iter(self.s, rect)\n",
        "        return next(scanline_strips)\n",
        "\n",
        "    def _line_settings(self, entry_pressure, pressure):\n",
        "        p1 = entry_pressure\n",
        "        p2 = (entry_pressure + pressure) / 2\n",
        "        p3 = pressure\n",
        "        if self.head == 0.0001:\n",
        "            p1 = p2\n",
        "        prange1 = p2 - p1\n",
        "        prange2 = p3 - p2\n",
        "        return p1, p2, prange1, prange2, self.head, self.tail\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "r2sqbc-4D_pk",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# mnist environment"
      ]
    },
    {
      "metadata": {
        "id": "zXATLvvnmGcW",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class MNISTDispenser():\n",
        "\n",
        "    def __init__(self, data_dir=Path('data'), screen_size=64):\n",
        "        self.data_dir = data_dir\n",
        "        self.height, self.width = screen_size, screen_size\n",
        "        self.prepare_mnist()\n",
        "\n",
        "    def get_random_target(self, num=1, squeeze=False):\n",
        "        random_idxes = np.random.choice(self.real_data.shape[0], num, replace=False)\n",
        "        random_image = self.real_data[random_idxes]\n",
        "        if squeeze:\n",
        "            random_image = np.squeeze(random_image, 0)\n",
        "        return random_image\n",
        "\n",
        "    def prepare_mnist(self):\n",
        "        ut.io.makedirs(self.data_dir)\n",
        "\n",
        "        # ground truth MNIST data\n",
        "        mnist_dir = self.data_dir / 'mnist'\n",
        "        mnist = tf.contrib.learn.datasets.DATASETS['mnist'](str(mnist_dir))\n",
        "\n",
        "        pkl_path = mnist_dir / 'mnist_dict.pkl'\n",
        "\n",
        "        if pkl_path.exists():\n",
        "            mnist_dict = ut.io.load_pickle(pkl_path)\n",
        "        else:\n",
        "            mnist_dict = defaultdict(lambda: defaultdict(list))\n",
        "            for name in ['train', 'test', 'valid']:\n",
        "                for num in range(10):\n",
        "                    filtered_data = \\\n",
        "                            mnist.train.images[mnist.train.labels == num]\n",
        "                    filtered_data = \\\n",
        "                            np.reshape(filtered_data, [-1, 28, 28])\n",
        "\n",
        "                    iterator = tqdm(filtered_data,\n",
        "                                    desc=\"[{}] Processing {}\".format(name, num))\n",
        "                    for idx, image in enumerate(iterator):\n",
        "                        # XXX: don't know which way would be the best\n",
        "                        resized_image = ut.io.imresize(\n",
        "                                image, [self.height, self.width],\n",
        "                                interp='cubic')\n",
        "                        mnist_dict[name][num].append(\n",
        "                                np.expand_dims(resized_image, -1))\n",
        "            ut.io.dump_pickle(pkl_path, mnist_dict)\n",
        "\n",
        "        mnist_dict = mnist_dict['train'] # as opposed to test\n",
        "\n",
        "        data = []\n",
        "        for num in range(10):\n",
        "            data.append(mnist_dict[int(num)])\n",
        "\n",
        "        self.real_data = 255 - np.concatenate([d for d in data])\n",
        "        self.real_data_labels = np.sort(mnist.train.labels)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "kLMmcTHo7YT4",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "mnist_dispenser = MNISTDispenser()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "l6I5xeZ6GUC-",
        "colab_type": "code",
        "outputId": "b54c1481-a7db-4a93-fd0e-d6149c193892",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "mnist_dispenser.get_random_target().shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1, 64, 64, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "metadata": {
        "id": "gMvNdKxPD0RZ",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# VAE Painter"
      ]
    },
    {
      "metadata": {
        "id": "k15-v4XxD1gU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class ConvVAE(object):\n",
        "  def __init__(self, z_size=64, batch_size=100, learning_rate=0.0001, kl_tolerance=0.5, is_training=True, reuse=False, gpu_mode=True, graph=None):\n",
        "    self.z_size = z_size\n",
        "    self.batch_size = batch_size\n",
        "    self.learning_rate = learning_rate\n",
        "    self.is_training = is_training\n",
        "    self.kl_tolerance = kl_tolerance\n",
        "    self.reuse = reuse\n",
        "    # Is it okay to comment this out? with tf.variable_scope('conv_vae', reuse=self.reuse):\n",
        "    if not gpu_mode:\n",
        "      with tf.device('/cpu:0'):\n",
        "        tf.logging.info('conv_vae using cpu.')\n",
        "        self._build_graph(graph)\n",
        "    else:\n",
        "      tf.logging.info('conv_vae using gpu.')\n",
        "      self._build_graph(graph)\n",
        "    self._init_session()\n",
        "  \n",
        "  def build_decoder(self, z, reuse=False):\n",
        "    with tf.variable_scope('decoder', reuse=reuse):\n",
        "      h = tf.layers.dense(z, 4*256, name=\"fc\")\n",
        "      h = tf.reshape(h, [-1, 1, 1, 4*256])\n",
        "      h = tf.layers.conv2d_transpose(h, 128, 5, strides=2, activation=tf.nn.relu, name=\"deconv1\")\n",
        "      h = tf.layers.conv2d_transpose(h, 64, 5, strides=2, activation=tf.nn.relu, name=\"deconv2\")\n",
        "      h = tf.layers.conv2d_transpose(h, 32, 6, strides=2, activation=tf.nn.relu, name=\"deconv3\")\n",
        "      return tf.layers.conv2d_transpose(h, 3, 6, strides=2, activation=tf.nn.sigmoid, name=\"deconv4\")\n",
        "  \n",
        "  def build_predictor(self, actions, reuse=False, is_training=False):\n",
        "    with tf.variable_scope('predictor', reuse=reuse):\n",
        "      h = tf.layers.dense(actions, 256, activation=tf.nn.leaky_relu, name=\"fc1\")\n",
        "      h = tf.layers.batch_normalization(h, training=is_training, name=\"bn1\")\n",
        "      h = tf.layers.dense(h, 64, activation=tf.nn.leaky_relu, name=\"fc2\")\n",
        "      h = tf.layers.batch_normalization(h, training=is_training, name=\"bn2\")\n",
        "      h = tf.layers.dense(h, 64, activation=tf.nn.leaky_relu, name=\"fc3\")\n",
        "      h = tf.layers.batch_normalization(h, training=is_training, name=\"bn3\")\n",
        "      return tf.layers.dense(h, self.z_size, name='fc4')\n",
        "  \n",
        "  def _build_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "    with self.g.as_default(), tf.variable_scope('conv_vae', reuse=self.reuse):\n",
        "\n",
        "      #### autoencoding part\n",
        "      self.x = tf.placeholder(tf.float32, shape=[None, 64, 64, 3])\n",
        "      \n",
        "      # Encoder\n",
        "      h = tf.layers.conv2d(self.x, 32, 4, strides=2, activation=tf.nn.relu, name=\"enc_conv1\")\n",
        "      h = tf.layers.conv2d(h, 64, 4, strides=2, activation=tf.nn.relu, name=\"enc_conv2\")\n",
        "      h = tf.layers.conv2d(h, 128, 4, strides=2, activation=tf.nn.relu, name=\"enc_conv3\")\n",
        "      h = tf.layers.conv2d(h, 256, 4, strides=2, activation=tf.nn.relu, name=\"enc_conv4\")\n",
        "      h = tf.reshape(h, [-1, 2*2*256])\n",
        "\n",
        "      # VAE\n",
        "      self.mu = tf.layers.dense(h, self.z_size, name=\"enc_fc_mu\")\n",
        "      self.logvar = tf.layers.dense(h, self.z_size, name=\"enc_fc_log_var\")\n",
        "      self.sigma = tf.exp(self.logvar / 2.0)\n",
        "      #self.epsilon = tf.random_normal([self.batch_size, self.z_size])\n",
        "      self.epsilon = tf.random_normal([tf.shape(self.sigma)[0], self.z_size])\n",
        "      self.z = self.mu + self.sigma * self.epsilon\n",
        "\n",
        "      # Decoder\n",
        "      self.y = self.build_decoder(self.z)\n",
        "      \n",
        "      #### predicting part\n",
        "      self.actions = tf.placeholder(tf.float32, shape=[None, 12])\n",
        "      self.predicted_z = self.build_predictor(self.actions, is_training=self.is_training)\n",
        "      self.predicted_y = self.build_decoder(self.predicted_z, reuse=True)\n",
        "      \n",
        "      # train ops\n",
        "      if self.is_training:\n",
        "        self.global_step = tf.Variable(0, name='global_step', trainable=False)\n",
        "        self.uneven_multiplier = tf.placeholder_with_default(1.0, [])\n",
        "        summ_uneven_mult = tf.summary.scalar('uneven_multiplier', self.uneven_multiplier)\n",
        "\n",
        "        eps = 1e-6 # avoid taking log of zero\n",
        "        \n",
        "        mask = tf.reduce_mean(\n",
        "          self.x,\n",
        "          reduction_indices = [3]\n",
        "        )\n",
        "        stroke_whitespace = tf.equal(mask, 1.0)\n",
        "        mask = tf.where(stroke_whitespace, tf.ones(tf.shape(mask)), self.uneven_multiplier*tf.ones(tf.shape(mask)))\n",
        "        mask = tf.reshape(mask, [-1, 64, 64, 1])\n",
        "        mask = tf.tile(mask, [1, 1, 1, 3])\n",
        "        summ_mask = tf.summary.image('mask', mask, max_outputs=3)\n",
        "        print(mask.get_shape())\n",
        "        \n",
        "        # reconstruction loss\n",
        "        self.r_loss = tf.reduce_sum(\n",
        "          (tf.square(self.x - self.y))*mask,\n",
        "          reduction_indices = [1,2,3]\n",
        "        )\n",
        "        self.r_loss = tf.reduce_mean(self.r_loss)\n",
        "\n",
        "        # augmented kl loss per dim\n",
        "        self.kl_loss = - 0.5 * tf.reduce_sum(\n",
        "          (1 + self.logvar - tf.square(self.mu) - tf.exp(self.logvar)),\n",
        "          reduction_indices = 1\n",
        "        )\n",
        "        self.kl_loss = tf.maximum(self.kl_loss, self.kl_tolerance * self.z_size)\n",
        "        self.kl_loss = tf.reduce_mean(self.kl_loss)\n",
        "        \n",
        "        summ_recon_loss = tf.summary.scalar('recon_loss', self.r_loss)\n",
        "        summ_kl_loss = tf.summary.scalar('kl_loss', self.kl_loss)\n",
        "        self.loss = self.r_loss + self.kl_loss\n",
        "        \n",
        "        # training the vae\n",
        "        self.lr = tf.Variable(self.learning_rate, trainable=False)\n",
        "        summ_lr = tf.summary.scalar('lr', self.lr)\n",
        "        self.optimizer = tf.train.AdamOptimizer(self.lr)\n",
        "        grads = self.optimizer.compute_gradients(\n",
        "            self.loss, \n",
        "            tf.global_variables('conv_vae/enc_*')+tf.global_variables('conv_vae/decoder*')) # can potentially clip gradients here.\n",
        "\n",
        "        self.train_op = self.optimizer.apply_gradients(\n",
        "          grads, global_step=self.global_step, name='train_step')\n",
        "        summ_loss = tf.summary.scalar('loss', self.loss)\n",
        "        \n",
        "        # training the predictor\n",
        "        self.predictor_loss = tf.reduce_mean(tf.square(self.predicted_z - self.z))\n",
        "        self.optimizer2 = tf.train.AdamOptimizer(self.lr)\n",
        "        grads2 = self.optimizer2.compute_gradients(self.predictor_loss, tf.global_variables('conv_vae/predictor*'))\n",
        "        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
        "          self.train_op2 = self.optimizer2.apply_gradients(\n",
        "            grads2, global_step=self.global_step, name='train_step2')\n",
        "        summ_predictor_loss = tf.summary.scalar('predictor_loss', self.predictor_loss)\n",
        "      \n",
        "        #summary ops\n",
        "        summ_inp_img = tf.summary.image('inp_img', self.x, max_outputs=3)\n",
        "        summ_output_img = tf.summary.image('output_img', self.y, max_outputs=3)\n",
        "        summ_predicted_output_img = tf.summary.image('predicted_output_img', self.predicted_y, max_outputs=3)\n",
        "        self.summary_op = tf.summary.merge([\n",
        "            summ_inp_img, summ_output_img, summ_loss, summ_kl_loss, summ_recon_loss,\n",
        "            summ_mask, summ_uneven_mult, summ_lr\n",
        "        ])\n",
        "        self.summary_op_2 = tf.summary.merge([\n",
        "            summ_inp_img, summ_predictor_loss, summ_output_img, summ_predicted_output_img, summ_lr\n",
        "        ])\n",
        "        \n",
        "\n",
        "      # initialize vars\n",
        "      self.init = tf.global_variables_initializer()\n",
        "  \n",
        "  def generate_stroke_graph(self, actions):\n",
        "    with tf.variable_scope('conv_vae', reuse=True):\n",
        "      with self.g.as_default():\n",
        "        # Encoder?\n",
        "        z = self.build_predictor(actions, reuse=True, is_training=False)\n",
        "\n",
        "        # Decoder\n",
        "        return self.build_decoder(z, reuse=True)\n",
        "\n",
        "  def _init_session(self):\n",
        "    \"\"\"Launch TensorFlow session and initialize variables\"\"\"\n",
        "    self.sess = tf.Session(graph=self.g)\n",
        "    self.sess.run(self.init)\n",
        "  def close_sess(self):\n",
        "    \"\"\" Close TensorFlow session \"\"\"\n",
        "    self.sess.close()\n",
        "  def save_model(self, model_save_path='tf_vae'):\n",
        "    sess = self.sess\n",
        "    step = sess.run(self.global_step)\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    checkpoint_path = os.path.join(model_save_path, 'vae')\n",
        "    tf.logging.info('saving model %s.', checkpoint_path)\n",
        "    saver.save(sess, checkpoint_path, step) # just keep one\n",
        "  def load_checkpoint(self, checkpoint_path='tf_vae', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)\n",
        "  def load_only_vae_checkpoint(self, checkpoint_path='tf_vae', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      to_save = tf.global_variables('conv_vae/enc_*')+tf.global_variables('conv_vae/decoder*')+tf.global_variables('conv_vae/conv_vae/enc_*')+tf.global_variables('conv_vae/conv_vae/decoder*')+tf.global_variables('conv_vae/global_step')\n",
        "      print(to_save)\n",
        "      saver = tf.train.Saver(to_save)\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "jSF1EvQvGUUj",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# GAN painter"
      ]
    },
    {
      "metadata": {
        "id": "axiRNmrtlcAK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def leaky_relu(x, alpha=0.2):\n",
        "    return tf.maximum(tf.minimum(0.0, alpha * x), x)\n",
        "\n",
        "def leaky_relu_batch_norm(x, alpha=0.2):\n",
        "    return leaky_relu(tf.contrib.layers.batch_norm(x, updates_collections=None), alpha)\n",
        "\n",
        "def relu_batch_norm(x):\n",
        "    return tf.nn.relu(tf.contrib.layers.batch_norm(x, updates_collections=None))\n",
        "  \n",
        "class DiscriminatorConditionalWM(object):\n",
        "    def __init__(self, divisor=1):\n",
        "        \"\"\"\n",
        "        make the network smaller by divisor times\n",
        "        \"\"\"\n",
        "        self.x_dim = 64 * 64 * 3\n",
        "        self.name = 'lsun/dcgan/d_net'\n",
        "        self.divisor=divisor\n",
        "        \n",
        "    def __call__(self, x, conditions, reuse=True):\n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            if reuse:\n",
        "                vs.reuse_variables()\n",
        "            bs = tf.shape(x)[0]\n",
        "            x = tf.reshape(x, [bs, 64, 64, 3])\n",
        "            conditions = tf.reshape(conditions, [bs, 12])\n",
        "            conditions = tf.contrib.layers.fully_connected(conditions, 64/self.divisor)\n",
        "            conditions = tf.reshape(conditions, [bs, 1, 1, 64/self.divisor])\n",
        "            conv1 = tcl.conv2d(\n",
        "                x, 64/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu\n",
        "            )\n",
        "            conv1 = conv1 + conditions\n",
        "            conv2 = tcl.conv2d(\n",
        "                conv1, 128/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv3 = tcl.conv2d(\n",
        "                conv2, 256/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.conv2d(\n",
        "                conv3, 512/self.divisor, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.flatten(conv4)\n",
        "            fc = tcl.fully_connected(conv4, 1, activation_fn=tf.identity)\n",
        "            return fc\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]\n",
        "\n",
        "class GeneratorConditional(object):\n",
        "    def __init__(self, divisor=1, add_noise=False):\n",
        "        self.x_dim = 64 * 64 * 3\n",
        "        self.divisor=divisor\n",
        "        self.name = 'lsun/dcgan/g_net'\n",
        "        self.add_noise = add_noise\n",
        "\n",
        "    def __call__(self, conditions, is_training):\n",
        "        with tf.contrib.framework.arg_scope([tcl.batch_norm], \n",
        "                                            is_training=is_training):\n",
        "          with tf.variable_scope(self.name) as vs:\n",
        "              bs = tf.shape(conditions)[0]\n",
        "              if self.add_noise:\n",
        "                conditions = tf.concat([conditions, tf.random.uniform([bs, 10])], axis=1)\n",
        "              fc = tcl.fully_connected(conditions, 4 * 4 * 1024/self.divisor, activation_fn=tf.identity)\n",
        "              conv1 = tf.reshape(fc, tf.stack([bs, 4, 4, 1024/self.divisor]))\n",
        "              conv1 = relu_batch_norm(conv1)\n",
        "              conv2 = tcl.conv2d_transpose(\n",
        "                  conv1, 512/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv3 = tcl.conv2d_transpose(\n",
        "                  conv2, 256/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv4 = tcl.conv2d_transpose(\n",
        "                  conv3, 128/self.divisor, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=relu_batch_norm\n",
        "              )\n",
        "              conv5 = tcl.conv2d_transpose(\n",
        "                  conv4, 3, [4, 4], [2, 2],\n",
        "                  weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                  activation_fn=tf.sigmoid)\n",
        "              return conv5\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]\n",
        "      \n",
        "class ConvGAN(object):\n",
        "  def __init__(self, learning_rate=0.0001, is_training=True, reuse=False, gpu_mode=True, data_glob='data/episodes_*.npz', divisor=1, add_noise=False, graph=None):\n",
        "    self.learning_rate = learning_rate\n",
        "    self.is_training = is_training\n",
        "    self.reuse = reuse\n",
        "    self.g_net = GeneratorConditional(divisor=divisor, add_noise=add_noise)\n",
        "    self.d_net = DiscriminatorConditionalWM(divisor=divisor)\n",
        "    \n",
        "    if self.is_training:\n",
        "      self.episode_files = tf.gfile.Glob(data_glob)\n",
        "      np.random.shuffle(self.episode_files)\n",
        "      self.ep_file_ctr = 0\n",
        "      self.current_file_idx = 0\n",
        "      loaded = np.load(self.episode_files[0])\n",
        "      self.loaded_strokes = loaded['strokes']\n",
        "      self.loaded_actions = loaded['actions']\n",
        "      self.len_loaded = len(self.loaded_strokes)\n",
        "    \n",
        "    if not gpu_mode:\n",
        "      with tf.device('/cpu:0'):\n",
        "        tf.logging.info('conv_gan using cpu.')\n",
        "        self._build_training_or_generator_graph(graph)\n",
        "    else:\n",
        "      tf.logging.info('conv_gan using gpu.')\n",
        "      self._build_training_or_generator_graph(graph)\n",
        "    self._init_session()\n",
        "   \n",
        "  def _build_training_or_generator_graph(self, graph):\n",
        "    if self.is_training:\n",
        "      self._build_graph(graph)\n",
        "    else:\n",
        "      self._build_generator_graph(graph)\n",
        "  \n",
        "  def _build_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "    with self.g.as_default(), tf.variable_scope('conv_gan', reuse=self.reuse):\n",
        "\n",
        "      self.x = tf.placeholder(tf.float32, shape=[None, 64, 64, 3])\n",
        "      self.actions = tf.placeholder(tf.float32, shape=[None, 12])\n",
        "      \n",
        "      self.y = self.g_net(self.actions, is_training=self.is_training)\n",
        "      \n",
        "      real_score = tf.reduce_mean(self.d_net(self.x, self.actions, reuse=False))\n",
        "      generated_score = tf.reduce_mean(self.d_net(self.y, self.actions))\n",
        "      \n",
        "      real_score_summ = tf.summary.scalar('real_score', real_score)\n",
        "      g_loss_summ = tf.summary.scalar('generated_score/g_loss', generated_score)\n",
        "      \n",
        "      reconstruction_loss = tf.reduce_mean(tf.square(self.y-self.x))\n",
        "      recon_loss_summ = tf.summary.scalar('recon_loss', reconstruction_loss)\n",
        "      \n",
        "      # actual reconstruction loss used for training\n",
        "      self.uneven_multiplier = tf.placeholder_with_default(1.0, [])\n",
        "      u_m_summ = tf.summary.scalar('uneven_multiplier', self.uneven_multiplier)\n",
        "      mask = tf.reduce_mean(\n",
        "        self.x,\n",
        "        reduction_indices = [3]\n",
        "      )\n",
        "      stroke_whitespace = tf.equal(mask, 1.0)\n",
        "      mask = tf.where(stroke_whitespace, tf.ones(tf.shape(mask)), self.uneven_multiplier*tf.ones(tf.shape(mask)))\n",
        "      mask = tf.reshape(mask, [-1, 64, 64, 1])\n",
        "      mask = tf.tile(mask, [1, 1, 1, 3])\n",
        "      tf.summary.image('mask', mask, max_outputs=3)\n",
        "      print(mask.get_shape())\n",
        "      self.r_loss = 10*tf.reduce_mean((tf.square(self.x - self.y))*mask)\n",
        "      actual_recon_loss_summ = tf.summary.scalar('recon_loss_actual', self.r_loss)\n",
        "      # /actual reconstruction loss used for training\n",
        "      \n",
        "      self.g_loss = generated_score + self.r_loss\n",
        "      self.d_loss = real_score - generated_score\n",
        "      \n",
        "      epsilon = tf.random_uniform([], 0.0, 1.0)\n",
        "      x_hat = epsilon * self.x + (1 - epsilon) * self.y\n",
        "      d_hat = self.d_net(x_hat, self.actions)\n",
        "      \n",
        "      ddx = tf.gradients(d_hat, x_hat)[0]\n",
        "      print(ddx.get_shape().as_list())\n",
        "\n",
        "      ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=(1, 2, 3)))\n",
        "      ddx = tf.reduce_mean(tf.square(ddx - 1.0) * 10.0)\n",
        "      \n",
        "      self.d_loss = self.d_loss + ddx\n",
        "      \n",
        "      gradient_penalty_summ = tf.summary.scalar('gradient_penalty', ddx)\n",
        "      d_loss_summ = tf.summary.scalar('actual_loss', self.d_loss)\n",
        "      \n",
        "      # train ops\n",
        "      if self.is_training:\n",
        "        self.global_step = tf.Variable(0, name='global_step', trainable=False)\n",
        "        self.inc_global_step = tf.assign(self.global_step, self.global_step+1)\n",
        "\n",
        "        self.d_adam, self.g_adam = None, None\n",
        "        with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
        "            self.d_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "                .minimize(self.d_loss, var_list=self.d_net.vars)\n",
        "            self.g_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "                .minimize(self.g_loss, var_list=self.g_net.vars)\n",
        "\n",
        "      # initialize vars\n",
        "      self.init = tf.global_variables_initializer()\n",
        "      \n",
        "      #summary ops\n",
        "      tf.summary.image('inp_img', self.x, max_outputs=3)\n",
        "      tf.summary.image('output_img', self.y, max_outputs=3)\n",
        "      self.summary_op = tf.summary.merge_all()\n",
        "      \n",
        "  def _build_generator_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "      \n",
        "    with self.g.as_default(), tf.variable_scope('conv_gan', reuse=self.reuse):\n",
        "      self.actions = tf.placeholder(tf.float32, shape=[None, 12])\n",
        "      self.y = self.g_net(self.actions, is_training=self.is_training)\n",
        "      self.init = tf.global_variables_initializer()\n",
        "  \n",
        "  def generate_stroke_graph(self, actions):\n",
        "    with tf.variable_scope('conv_gan', reuse=True):\n",
        "      with self.g.as_default():\n",
        "        return self.g_net(actions, is_training=False)\n",
        "      \n",
        "  def get_random_batch(self, batch_size):\n",
        "    while self.len_loaded - self.current_file_idx < batch_size:\n",
        "      self.ep_file_ctr = (self.ep_file_ctr + 1) % len(self.episode_files)\n",
        "      self.current_file_idx = 0\n",
        "      print('loading new file', self.episode_files[self.ep_file_ctr])\n",
        "      loaded = np.load(self.episode_files[self.ep_file_ctr])\n",
        "      self.loaded_strokes = loaded['strokes']\n",
        "      self.loaded_actions = loaded['actions']\n",
        "      self.len_loaded = len(self.loaded_strokes)\n",
        "      rand_idx = np.random.permutation(self.len_loaded)\n",
        "      self.loaded_strokes = self.loaded_strokes[rand_idx]\n",
        "      self.loaded_actions = self.loaded_actions[rand_idx]\n",
        "    \n",
        "    strokes = self.loaded_strokes[self.current_file_idx:self.current_file_idx+batch_size]\n",
        "    strokes = strokes.astype(np.float)/255.0\n",
        "    actions = self.loaded_actions[self.current_file_idx:self.current_file_idx+batch_size]\n",
        "    \n",
        "    self.current_file_idx += batch_size\n",
        "    \n",
        "    return strokes, actions\n",
        "      \n",
        "  def train(self, batch_size=64, num_batches=1000000):\n",
        "    train_writer = tf.summary.FileWriter('logdir', self.g)\n",
        "    start_time = time.time()\n",
        "    uneven_multiplier = 10.\n",
        "    for t in range(num_batches):\n",
        "        d_iters = 5\n",
        "\n",
        "        for _ in range(0, d_iters):\n",
        "            strokes, actions = self.get_random_batch(batch_size)\n",
        "            _, summ = self.sess.run((self.d_adam, self.summary_op), \n",
        "                                    feed_dict={self.x: strokes, self.actions: actions})\n",
        "\n",
        "        strokes, actions = self.get_random_batch(batch_size)\n",
        "        _, summ, _, step = self.sess.run((self.g_adam, self.summary_op, self.inc_global_step, self.global_step), \n",
        "                                      feed_dict={self.x: strokes, \n",
        "                                                 self.actions: actions, \n",
        "                                                 self.uneven_multiplier: uneven_multiplier})\n",
        "        train_writer.add_summary(summ, step)\n",
        "        \n",
        "        if step > 295044:\n",
        "          uneven_multiplier = 1.\n",
        "\n",
        "        if step % 20 == 0:\n",
        "            strokes, actions = self.get_random_batch(batch_size)\n",
        "            d_loss = self.sess.run(\n",
        "                self.d_loss, feed_dict={self.x: strokes, self.actions: actions}\n",
        "            )\n",
        "            g_loss = self.sess.run(\n",
        "                self.g_loss, feed_dict={self.x: strokes, self.actions: actions}\n",
        "            )\n",
        "            print('Iter [%8d] Time [%5.4f] d_loss [%.4f] g_loss [%.4f]' %\n",
        "                    (step, time.time() - start_time, d_loss, g_loss))\n",
        "            \n",
        "        if step % 2000 == 0:\n",
        "            self.save_model(\"tf_gan3\")\n",
        "\n",
        "  def _init_session(self):\n",
        "    \"\"\"Launch TensorFlow session and initialize variables\"\"\"\n",
        "    self.sess = tf.Session(graph=self.g)\n",
        "    self.sess.run(self.init)\n",
        "  def close_sess(self):\n",
        "    \"\"\" Close TensorFlow session \"\"\"\n",
        "    self.sess.close()\n",
        "  def save_model(self, model_save_path='tf_gan3'):\n",
        "    sess = self.sess\n",
        "    step = sess.run(self.global_step)\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    checkpoint_path = os.path.join(model_save_path, 'gan')\n",
        "    tf.logging.info('saving model %s.', checkpoint_path)\n",
        "    saver.save(sess, checkpoint_path, step) # just keep one\n",
        "  def load_checkpoint(self, checkpoint_path='tf_gan3', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "g02M6O6e8IiK",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Generate human samples"
      ]
    },
    {
      "metadata": {
        "id": "tcGK-yqaBXZp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_images(images):\n",
        "  h=args.screen_size\n",
        "  fig=plt.figure(figsize=(16, 16))\n",
        "  columns = len(images)\n",
        "  rows = 1\n",
        "\n",
        "  for i, img in enumerate(images):\n",
        "    img = img[:, :, :3]\n",
        "    #print(img.shape)\n",
        "    fig.add_subplot(rows, columns, i+1)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(img)\n",
        "  plt.show()\n",
        "\n",
        "def run_actions_on_real_env(realEnv, actions_array):\n",
        "  images = []\n",
        "  realEnv.reset()\n",
        "  for ac in actions_array:\n",
        "    print(ac)\n",
        "    realEnv.draw(ac)\n",
        "    images.append(realEnv.image)\n",
        "  return images"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "nSy9xU1G-4l5",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_realEnv = ColorEnv(args, paint_mode=PaintMode.CONNECTED_STROKES)\n",
        "_realEnv.reset()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0yR-eI2c-4gc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_input_actions = np.array(\n",
        "  [\n",
        "      [\n",
        "          [1.0, 1.0, 0.5, 0.8, 0.5, 0.8, 0, 0, 0, 0.5, 0.8, 0],\n",
        "          [0.5, 0.5, 0.5, 0.8, 0.5, 0.8, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.3, 0.6, 0.3, 0.6, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.3, 0.4, 0.3, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.2, 0.5, 0.2, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.7, 0.4, 0.7, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.7, 0.6, 0.7, 0.6, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.8, 0.5, 0.8, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.2, 0.4, 0.2, 0.4, 0, 0, 0, 0.2, 0.4, 0],\n",
        "          [0.5, 0.5, 0.2, 0.4, 0.2, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.15, 0.5, 0.15, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.9, 0.5, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.2, 0.9, 0.2, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.9, 0.5, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.8, 0.9, 0.8, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.2, 0.4, 0.2, 0.4, 0, 0, 0, 0.2, 0.4, 0],\n",
        "          [0.5, 0.5, 0.2, 0.4, 0.2, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.15, 0.5, 0.15, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.8, 0.4, 0.8, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.7, 0.7, 0.5, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.2, 0.9, 0.2, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.1, 0.7, 0.2, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.3, 0.6, 0.8, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0.2, 0.2, 0],\n",
        "          [0.5, 0.5, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.15, 0.5, 0.15, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.75, 0.3, 0.5, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.35, 0.6, 0.2, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.35, 0.4, 0.5, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.8, 0.6, 0.8, 0.8, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.9, 0.3, 0.8, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.5, 0.2, 0.5, 0.2, 0, 0, 0, 0.5, 0.2, 0],\n",
        "          [0.5, 0.5, 0.5, 0.2, 0.5, 0.2, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.2, 0.4, 0.2, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.7, 0.5, 0.7, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.0, 0.0, 0.6, 0.15, 0.6, 0.15, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.6, 0.15, 0.6, 0.15, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.6, 0.5, 0.6, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.6, 0.9, 0.6, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.7, 0.2, 0.7, 0.2, 0, 0, 0, 0.7, 0.2, 0],\n",
        "          [0.5, 0.5, 0.7, 0.2, 0.7, 0.2, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.3, 0.3, 0.3, 0.3, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.3, 0.25, 0.3, 0.25, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.3, 0.4, 0.3, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.6, 0.5, 0.6, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.6, 0.7, 0.6, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.2, 0.9, 0.2, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.5, 0.3, 0.5, 0.3, 0, 0, 0, 0.5, 0.3, 0],\n",
        "          [0.5, 0.5, 0.5, 0.3, 0.5, 0.3, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.4, 0.3, 0.3, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.3, 0.7, 0.5, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.8, 0.7, 0.8, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.65, 0.5, 0.5, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.4, 0.8, 0.4, 0.8, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.4, 0.9, 0.4, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.3, 0.3, 0.3, 0.3, 0, 0, 0, 0.3, 0.3, 0],\n",
        "          [0.5, 0.5, 0.3, 0.3, 0.3, 0.3, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.2, 0.2, 0.2, 0.2, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.7, 0.3, 0.7, 0.3, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.6, 0.5, 0.6, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.3, 0.9, 0.3, 0.9, 0, 0, 0, 0, 0, 0],\n",
        "          [0.0, 0.0, 0.5, 0.7, 0.5, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.55, 0.7, 0.55, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.3, 0.2, 0.3, 0.2, 0, 0, 0, 0.3, 0.2, 0],\n",
        "          [0.5, 0.5, 0.3, 0.2, 0.3, 0.2, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.1, 0.3, 0.25, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.7, 0.7, 0.7, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.9, 0.3, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.5, 0.5, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.7, 0.3, 0.7, 0.3, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.2, 0.5, 0.2, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "      [\n",
        "          [1.0, 1.0, 0.8, 0.5, 0.8, 0.5, 0, 0, 0, 0.8, 0.5, 0],\n",
        "          [0.5, 0.5, 0.8, 0.5, 0.8, 0.5, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.5, 0.5, 0.4, 0.3, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.6, 0.1, 0.8, 0.1, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.8, 0.2, 0.8, 0.2, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.75, 0.4, 0.75, 0.4, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.7, 0.7, 0.7, 0.7, 0, 0, 0, 0, 0, 0],\n",
        "          [0.5, 0.5, 0.65, 0.8, 0.65, 0.8, 0, 0, 0, 0, 0, 0],\n",
        "      ],\n",
        "  ]\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sCZy1oJ1Bbzt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\"\"\"\n",
        "actions_to_idx = {\n",
        "        'pressure': 0,\n",
        "        'size': 1,\n",
        "        'control_x': 2,\n",
        "        'control_y': 3,\n",
        "        'end_x': 4,\n",
        "        'end_y': 5,\n",
        "        'color_r': 6,\n",
        "        'color_g': 7,\n",
        "        'color_b': 8,\n",
        "        'start_x': 9,\n",
        "        'start_y': 10,\n",
        "        'entry_pressure': 11,\n",
        "    }\n",
        "\"\"\"\n",
        "\n",
        "_generated_image = []\n",
        "_intermediate_real_canvases = []\n",
        "for i in range(len(_input_actions)):\n",
        "  _real_images = run_actions_on_real_env(_realEnv, _input_actions[i])\n",
        "  _generated_image.append(_real_images[-1])\n",
        "  _intermediate_real_canvases.append(_real_images)\n",
        "  if i in range(10):\n",
        "    plot_images(_intermediate_real_canvases[i])\n",
        "  \n",
        "_generated_image = np.array(_generated_image)[:, :, :, :3].astype(np.float)/255.\n",
        "plot_images(_generated_image)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rMIKrbFXaDdk",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_stacked_plots = []\n",
        "for i in range(len(_input_actions)):\n",
        "  run_actions_on_real_env(_realEnv, _input_actions[i])\n",
        "  print(len(_realEnv.intermediate_images))\n",
        "  _realEnv.intermediate_images = _realEnv.intermediate_images[0::10]\n",
        "\n",
        "  _inter_images = np.stack(_realEnv.intermediate_images)[:, :, :, :3]\n",
        "  \n",
        "  _DELAY = 150\n",
        "  _inter_images = np.concatenate([_inter_images, np.tile(_inter_images[-1, :, :, :], [_DELAY-len(_inter_images), 1, 1, 1])], axis=0)\n",
        "  \n",
        "  print(len(_realEnv.intermediate_images))\n",
        "  print(_inter_images.shape)\n",
        "\n",
        "  _stacked_plots.append(_inter_images)\n",
        "\n",
        "_top_row = np.concatenate(_stacked_plots[:5], axis=2)\n",
        "_bot_row = np.concatenate(_stacked_plots[5:], axis=2)\n",
        "_both = np.concatenate([_top_row, _bot_row], axis=1)\n",
        "\n",
        "clip = mpy.ImageSequenceClip([x for x in _both], fps=14)\n",
        "clip.write_videofile(\"hello2.mp4\")\n",
        "display(mpy.ipython_display('hello2.mp4', height=200))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Gm--vvK2HC1P",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Adversarial Training graph"
      ]
    },
    {
      "metadata": {
        "id": "VZF5_lQat8oC",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def res_block(x, channel, size, name):\n",
        "    with tf.variable_scope(name):\n",
        "        enc_x = tf.contrib.layers.conv2d(\n",
        "                x, channel, size,\n",
        "                padding='same',\n",
        "                activation_fn=tf.nn.relu)\n",
        "\n",
        "        res = tf.contrib.layers.conv2d(\n",
        "                enc_x, channel, size,\n",
        "                padding='same',\n",
        "                activation_fn=None) + x\n",
        "    return res\n",
        "\n",
        "class ActionGenerator2(object):\n",
        "    def __init__(self):\n",
        "        self.name = 'action_generator2'\n",
        "        self.cell = None\n",
        "        \n",
        "    def create_cell(self):\n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            self.cell = tf.nn.rnn_cell.LSTMCell(256)\n",
        "            return self.cell\n",
        "\n",
        "    def __call__(self, prev_cell_state, prev_hidden_state, prev_actions, current_canvas, target_image, reuse=True):\n",
        "        if self.cell is None:\n",
        "            self.create_cell()\n",
        "            \n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            if reuse:\n",
        "                vs.reuse_variables()\n",
        "            bs = tf.shape(prev_hidden_state)[0]\n",
        "            \n",
        "            prev_actions = tf.contrib.layers.fully_connected(prev_actions, 32)\n",
        "            concat_actions_world = tf.reshape(prev_actions, [bs, 1, 1, 32])\n",
        "            \n",
        "            current_canvas = tf.reshape(current_canvas, [bs, 64, 64, 3])\n",
        "            target_image = tf.reshape(target_image, [bs, 64, 64, 3])\n",
        "            concat_canvas = tf.concat([current_canvas, target_image], axis=3)  # Does this make sense? idk\n",
        "            \n",
        "            concat_canvas = tf.contrib.layers.conv2d(\n",
        "              concat_canvas, 32, 5, scope=\"x_c_enc\"\n",
        "            )\n",
        "            \n",
        "            conditioned_canvas = concat_canvas + concat_actions_world\n",
        "            \n",
        "            for idx in range(int(3)):\n",
        "                conditioned_canvas = tf.contrib.layers.conv2d(\n",
        "                        conditioned_canvas, 32, 4, stride=(2, 2),\n",
        "                        padding='valid',\n",
        "                        activation_fn=tf.nn.relu,\n",
        "                        scope=\"add_enc_{}\".format(idx))\n",
        "            for idx in range(8):\n",
        "                conditioned_canvas = res_block(\n",
        "                        conditioned_canvas, 32, 3,\n",
        "                        name=\"encoder_res_{}\".format(idx))\n",
        "                \n",
        "            conditioned_canvas = tf.contrib.layers.flatten(conditioned_canvas)\n",
        "            \n",
        "            lstm_in = tf.contrib.layers.fully_connected(conditioned_canvas, 256)\n",
        "            \n",
        "            lstm_out, lstm_state = self.cell(lstm_in, tf.nn.rnn_cell.LSTMStateTuple(prev_cell_state, prev_hidden_state))\n",
        "            \n",
        "            fc = tf.contrib.layers.fully_connected(lstm_out, 12, activation_fn=tf.sigmoid)\n",
        "            #fc = tf.concat([tf.zeros([bs, 1]), fc[:, 1:]], axis=1)\n",
        "            fc = tf.concat([\n",
        "                fc[:, :6],\n",
        "                tf.zeros([bs, 3]),\n",
        "                fc[:, 9:]\n",
        "            ], axis=1)\n",
        "            return fc, lstm_state\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]\n",
        "\n",
        "class DiscriminatorConditional(object):\n",
        "    def __init__(self):\n",
        "        self.x_dim = 64 * 64 * 3\n",
        "        self.name = 'lsun/dcgan/d_net'\n",
        "\n",
        "    def __call__(self, x, target, reuse=True):\n",
        "        with tf.variable_scope(self.name) as vs:\n",
        "            if reuse:\n",
        "                vs.reuse_variables()\n",
        "            bs = tf.shape(x)[0]\n",
        "            x = tf.reshape(x, [bs, 64, 64, 3])\n",
        "            target = tf.reshape(target, [bs, 64, 64, 3])\n",
        "            x = tf.concat([x, target], axis=3)\n",
        "            conv1 = tcl.conv2d(\n",
        "                x, 64, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu\n",
        "            )\n",
        "            conv2 = tcl.conv2d(\n",
        "                conv1, 128, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv3 = tcl.conv2d(\n",
        "                conv2, 256, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.conv2d(\n",
        "                conv3, 512, [4, 4], [2, 2],\n",
        "                weights_initializer=tf.random_normal_initializer(stddev=0.02),\n",
        "                activation_fn=leaky_relu_batch_norm\n",
        "            )\n",
        "            conv4 = tcl.flatten(conv4)\n",
        "            fc = tcl.fully_connected(conv4, 1, activation_fn=tf.identity)\n",
        "            return fc\n",
        "\n",
        "    @property\n",
        "    def vars(self):\n",
        "        return [var for var in tf.global_variables() if self.name in var.name]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "e_mGXBC3HLEp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "class AdversarialGraph(object):\n",
        "  def __init__(self, inp_synth_image_acs, num_unique_labels, max_seq_len=8, painter_type=\"VAE\", connected=True, gpu_mode=True, graph=None):\n",
        "    self.inp_synth_image_acs = inp_synth_image_acs\n",
        "    self.num_unique_labels = num_unique_labels\n",
        "    self.painter_type=painter_type\n",
        "    self.connected = connected\n",
        "    self.action_generator = ActionGenerator2()\n",
        "    self.d_net = DiscriminatorConditional()\n",
        "    self.data_dispenser = MNISTDispenser()\n",
        "    self.max_seq_len = max_seq_len\n",
        "    \n",
        "    self.gpu_mode = gpu_mode\n",
        "    if not gpu_mode:\n",
        "      with tf.device('/cpu:0'):\n",
        "        tf.logging.info('Model using cpu.')\n",
        "        self._build_graph(graph)\n",
        "    else:\n",
        "      tf.logging.info('Model using gpu.')\n",
        "      self._build_graph(graph)\n",
        "    self._init_session()\n",
        "  \n",
        "  def get_random_batch(self, batch_size):\n",
        "    random_idxes = np.random.choice(self.data_dispenser.real_data.shape[0], batch_size, replace=False)\n",
        "    random_targets = self.data_dispenser.real_data[random_idxes]\n",
        "    random_labels = self.data_dispenser.real_data_labels[random_idxes]\n",
        "    \n",
        "    random_targets = np.tile(random_targets, [1, 1, 1, 3]).astype(np.float)/255.\n",
        "    return random_targets, random_labels\n",
        "    \n",
        "    # sanity check\n",
        "    #self.target_np = self.mnist_env.real_data[1].reshape([1, 64, 64, 1])\n",
        "    #self.target_np_batch = np.tile(self.target_np, [batch_size,1,1,3]).astype(np.float)/255.0\n",
        "    #return self.target_np_batch\n",
        "  \n",
        "  def _build_graph(self, graph):\n",
        "    if graph is None:\n",
        "      self.g = tf.Graph()\n",
        "    else:\n",
        "      self.g = graph\n",
        "    \n",
        "    # Set up graphs of GAN\n",
        "    if self.painter_type == \"VAE\":\n",
        "      self.painter = ConvVAE(z_size=64, batch_size=100, \n",
        "                         learning_rate=1e-4, kl_tolerance=0.5, \n",
        "                         gpu_mode=self.gpu_mode, is_training=False, \n",
        "                         reuse=False, graph=self.g)\n",
        "    else:\n",
        "      self.painter = ConvGAN(learning_rate=1e-4,\n",
        "              is_training=False,\n",
        "              reuse=False,\n",
        "              gpu_mode=self.gpu_mode,\n",
        "              divisor=4,\n",
        "              add_noise=False,\n",
        "              graph=self.g)\n",
        "    self.painter.close_sess()\n",
        "    with self.g.as_default():\n",
        "      print(tf.global_variables())\n",
        "    \n",
        "    with self.g.as_default():\n",
        "      self.target_image = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 3])\n",
        "      self.target_image_labels = tf.placeholder(dtype=tf.int32, shape=[None])\n",
        "      self.synthetic_actions = tf.placeholder(dtype=tf.float32, shape=[self.max_seq_len, None, 12])\n",
        "      self.g_loss_scalar = tf.placeholder_with_default(1.0, shape=())\n",
        "      self.forcing_loss_scalar = tf.placeholder_with_default(1.0, shape=())\n",
        "      \n",
        "      g_loss_scalar_summ = tf.summary.scalar('g_loss_scalar', self.g_loss_scalar)\n",
        "      forcing_loss_scalar_summ = tf.summary.scalar('forcing_loss_scalar', self.forcing_loss_scalar)\n",
        "      \n",
        "      target_image_one_hot = tf.one_hot(self.target_image_labels, self.num_unique_labels)\n",
        "      \n",
        "      synthetic_batch_size = tf.shape(self.synthetic_actions)[1]\n",
        "      batch_size = tf.shape(self.target_image)[0]\n",
        "            \n",
        "      # Prepare loop vars for rnn loop\n",
        "      canvas_state = tf.ones(shape=[batch_size, 64, 64, 3], dtype=tf.float32)\n",
        "      actions = tf.zeros(shape=[batch_size, 12], dtype=tf.float32)\n",
        "      action_cell_state, action_hidden_state = self.action_generator.create_cell().zero_state(batch_size=batch_size, dtype=tf.float32)\n",
        "      i = tf.constant(0)\n",
        "      initial_ta = tf.TensorArray(dtype=tf.float32, size=self.max_seq_len)\n",
        "      initial_canvas_ta = tf.TensorArray(dtype=tf.float32, size=self.max_seq_len)\n",
        "      loop_vars = (\n",
        "          canvas_state, actions, action_cell_state, action_hidden_state, \n",
        "          initial_ta, initial_canvas_ta, i)\n",
        "      \n",
        "      # Just make the graph variables so we can use it in the loop. TODO: Can't I create this in the loop itself?\n",
        "      self.action_generator(action_cell_state, action_hidden_state, actions, canvas_state, canvas_state, reuse=False)\n",
        "      \n",
        "      # condition for continuation\n",
        "      def cond(cs, ac, acs, ahs, i_ta, c_ta, i):\n",
        "        return tf.less(i, self.max_seq_len)\n",
        "      \n",
        "      # run one state of rnn cell\n",
        "      def body(cs, ac, acs, ahs, i_ta, c_ta, i):\n",
        "        i = tf.add(i, 1)\n",
        "        \n",
        "        prev_ac = ac\n",
        "        ac, (acs, ahs) = self.action_generator(acs, ahs, ac, cs, self.target_image)\n",
        "        i_ta = i_ta.write(i-1, ac)\n",
        "\n",
        "        def use_whole_action():\n",
        "          return ac\n",
        "        \n",
        "        def use_previous_entrypoint():\n",
        "          # start x and y are previous end x and y\n",
        "          # start pressure is previous pressure\n",
        "          return tf.concat([ac[:, :9], prev_ac[:, 4:6], prev_ac[:, 0:1]], axis=1)\n",
        "        \n",
        "        if self.connected:\n",
        "          ac = tf.cond(tf.equal(i, 1), true_fn=use_whole_action, false_fn=use_previous_entrypoint)\n",
        "        else:\n",
        "          ac = use_whole_action()\n",
        "        \n",
        "        decoded_stroke = self.painter.generate_stroke_graph(ac)\n",
        "\n",
        "        darkness_mask = tf.reduce_mean(decoded_stroke, axis=3)\n",
        "        darkness_mask = 1 - tf.reshape(darkness_mask, [batch_size, 64, 64, 1])\n",
        "        darkness_mask = darkness_mask / tf.reduce_max(darkness_mask)\n",
        "        \n",
        "        color_action = ac[:, 6:9]\n",
        "        color_action = tf.reshape(color_action, [batch_size, 1, 1, 3])\n",
        "        color_action = tf.tile(color_action, [1, 64, 64, 1])\n",
        "        stroke_whitespace = tf.equal(decoded_stroke, 1.)\n",
        "        maxed_stroke = tf.where(stroke_whitespace, decoded_stroke, color_action)\n",
        "        \n",
        "        cs = (darkness_mask)*maxed_stroke + (1-darkness_mask)*cs\n",
        "        c_ta = c_ta.write(i-1, cs)\n",
        "        \n",
        "        return (cs, ac, acs, ahs, i_ta, c_ta, i)\n",
        "      \n",
        "      final_canvas_state, ac, _, _, final_ta, final_canvas_ta, _ = tf.while_loop(cond, body, loop_vars, swap_memory=True)\n",
        "      self.final_canvas_state = final_canvas_state\n",
        "      self.last_actions = final_ta.stack()\n",
        "      self.intermediate_canvases = final_canvas_ta.stack()\n",
        "      \n",
        "      real_score = tf.reduce_mean(self.d_net(self.target_image, self.target_image, reuse=False))\n",
        "      generated_score = tf.reduce_mean(self.d_net(final_canvas_state, self.target_image))\n",
        "      \n",
        "      real_score_summ = tf.summary.scalar('real score', real_score)\n",
        "      g_loss_summ = tf.summary.scalar('generated score/g_loss', generated_score)\n",
        "      \n",
        "      self.g_loss = generated_score\n",
        "      reconstruction_loss = 10*tf.reduce_mean(tf.square(final_canvas_state-self.target_image))\n",
        "      recon_loss_summ = tf.summary.scalar('recon_loss', reconstruction_loss)\n",
        "      #self.g_loss = self.g_loss + reconstruction_loss\n",
        "     \n",
        "      self.d_loss = real_score - generated_score\n",
        "      \n",
        "      real_min_gen_summ = tf.summary.scalar('real - generated', self.d_loss)\n",
        "      \n",
        "      epsilon = tf.random_uniform([], 0.0, 1.0)\n",
        "      x_hat = epsilon * self.target_image + (1 - epsilon) * final_canvas_state\n",
        "      d_hat = self.d_net(x_hat, self.target_image)\n",
        "      \n",
        "      # TODO: This might be wrong. might need to calc gradients wrt xhat concated with target\n",
        "      ddx = tf.gradients(d_hat, x_hat)[0]\n",
        "      print(ddx.get_shape().as_list())\n",
        "      #ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=1))\n",
        "      ddx = tf.sqrt(tf.reduce_sum(tf.square(ddx), axis=(1, 2, 3)))\n",
        "      ddx = tf.reduce_mean(tf.square(ddx - 1.0) * 10.0)\n",
        "      \n",
        "      self.d_loss = self.d_loss + ddx\n",
        "      \n",
        "      gradient_penalty_summ = tf.summary.scalar('gradient penalty', ddx)\n",
        "      d_loss_summ = tf.summary.scalar('actual loss', self.d_loss)\n",
        "      \n",
        "      # Extra loss for human-generated strokes\n",
        "      #forcing_loss = 1000 * tf.reduce_mean(tf.square(self.last_actions[:, :synthetic_batch_size, 2:6] - self.synthetic_actions[:, :, 2:6]))\n",
        "      forcing_loss = 2000 * tf.reduce_mean(tf.square(self.last_actions[:, :synthetic_batch_size, :] - self.synthetic_actions))\n",
        "      #forcing_loss = 4000 * tf.reduce_mean(tf.square(self.last_actions[:, :synthetic_batch_size, :] - self.synthetic_actions))\n",
        "      forcing_loss_summ = tf.summary.scalar('forcing_loss', forcing_loss)\n",
        "      self.g_loss = self.g_loss_scalar * self.g_loss + self.forcing_loss_scalar * forcing_loss\n",
        "        \n",
        "      self.d_adam, self.g_adam = None, None\n",
        "      with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):\n",
        "          self.d_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "              .minimize(self.d_loss, var_list=self.d_net.vars)\n",
        "          self.g_adam = tf.train.AdamOptimizer(learning_rate=1e-4, beta1=0.5, beta2=0.9)\\\n",
        "              .minimize(self.g_loss, var_list=self.action_generator.vars)\n",
        "        \n",
        "\n",
        "      # initialize vars\n",
        "      self.init = tf.global_variables_initializer()\n",
        "      \n",
        "      #summary ops\n",
        "      real_im_summ = tf.summary.image('real_images', self.target_image, max_outputs=3)\n",
        "      gen_im_summ = tf.summary.image('generated_images', final_canvas_state, max_outputs=3)\n",
        "      self.summary_op = tf.summary.merge([real_im_summ, gen_im_summ, d_loss_summ, gradient_penalty_summ, \n",
        "                                          real_min_gen_summ, g_loss_summ, real_score_summ,\n",
        "                                          recon_loss_summ, forcing_loss_summ,\n",
        "                                          g_loss_scalar_summ, forcing_loss_scalar_summ,\n",
        "                                         ])\n",
        "      \n",
        "  def train(self, batch_size=64, num_batches=1000000, last_actions_arr=[0, 1, 2], start_batch=0):\n",
        "    batches_before_starting = 2500\n",
        "    batches_before_removing_forcing_loss = 4500\n",
        "    assert batches_before_removing_forcing_loss > batches_before_starting\n",
        "    train_writer = tf.summary.FileWriter('logdir', self.g)\n",
        "    start_time = time.time()\n",
        "    for t in range(start_batch, num_batches):\n",
        "        if t > batches_before_starting:\n",
        "          current_g_loss_scalar = 1\n",
        "        else:\n",
        "          current_g_loss_scalar = 0\n",
        "        if t > batches_before_removing_forcing_loss:\n",
        "          current_forcing_loss_scalar = 0.2\n",
        "        else:\n",
        "          current_forcing_loss_scalar = 1\n",
        "          \n",
        "        if t > batches_before_starting:\n",
        "          d_iters = 5\n",
        "          for _ in range(0, d_iters):\n",
        "              rand_batch, rand_lab = self.get_random_batch(batch_size)\n",
        "              synthetic_indices = []\n",
        "              for i in range(batch_size):\n",
        "                if rand_lab[i] == 0: synthetic_indices.append(np.random.randint(0, 1))\n",
        "                elif rand_lab[i] == 1: synthetic_indices.append(np.random.randint(1, 2))\n",
        "                elif rand_lab[i] == 2: synthetic_indices.append(np.random.randint(2, 3))\n",
        "                elif rand_lab[i] == 3: synthetic_indices.append(np.random.randint(3, 4))\n",
        "                elif rand_lab[i] == 4: synthetic_indices.append(np.random.randint(4, 5))\n",
        "                elif rand_lab[i] == 5: synthetic_indices.append(np.random.randint(5, 6))\n",
        "                elif rand_lab[i] == 6: synthetic_indices.append(np.random.randint(6, 7))\n",
        "                elif rand_lab[i] == 7: synthetic_indices.append(np.random.randint(7, 8))\n",
        "                elif rand_lab[i] == 8: synthetic_indices.append(np.random.randint(8, 9))\n",
        "                elif rand_lab[i] == 9: synthetic_indices.append(np.random.randint(9, 10))\n",
        "              _, summ = self.sess.run((self.d_adam, self.summary_op),\n",
        "                                      feed_dict={self.target_image: rand_batch, \n",
        "                                                 self.target_image_labels: rand_lab,\n",
        "                                                 self.synthetic_actions: self.inp_synth_image_acs[:, synthetic_indices, :],\n",
        "                                                 self.g_loss_scalar: current_g_loss_scalar,\n",
        "                                                 self.forcing_loss_scalar: current_forcing_loss_scalar})\n",
        "\n",
        "        rand_batch, rand_lab = self.get_random_batch(batch_size)\n",
        "        synthetic_indices = []\n",
        "        for i in range(batch_size):\n",
        "          if rand_lab[i] == 0: synthetic_indices.append(np.random.randint(0, 1))\n",
        "          elif rand_lab[i] == 1: synthetic_indices.append(np.random.randint(1, 2))\n",
        "          elif rand_lab[i] == 2: synthetic_indices.append(np.random.randint(2, 3))\n",
        "          elif rand_lab[i] == 3: synthetic_indices.append(np.random.randint(3, 4))\n",
        "          elif rand_lab[i] == 4: synthetic_indices.append(np.random.randint(4, 5))\n",
        "          elif rand_lab[i] == 5: synthetic_indices.append(np.random.randint(5, 6))\n",
        "          elif rand_lab[i] == 6: synthetic_indices.append(np.random.randint(6, 7))\n",
        "          elif rand_lab[i] == 7: synthetic_indices.append(np.random.randint(7, 8))\n",
        "          elif rand_lab[i] == 8: synthetic_indices.append(np.random.randint(8, 9))\n",
        "          elif rand_lab[i] == 9: synthetic_indices.append(np.random.randint(9, 10))\n",
        "            \n",
        "        _, last_actions, fcstate, summ = self.sess.run((self.g_adam, self.last_actions, self.final_canvas_state, self.summary_op), \n",
        "                                                       feed_dict={self.target_image: rand_batch,\n",
        "                                                                  self.target_image_labels: rand_lab,\n",
        "                                               self.synthetic_actions: self.inp_synth_image_acs[:, synthetic_indices, :],\n",
        "                                                                  self.g_loss_scalar: current_g_loss_scalar,\n",
        "                                                                  self.forcing_loss_scalar: current_forcing_loss_scalar})\n",
        "        \n",
        "        last_actions_arr[0] = last_actions\n",
        "        last_actions_arr[1] = fcstate\n",
        "        last_actions_arr[2] = rand_batch\n",
        "        train_writer.add_summary(summ, t)\n",
        "\n",
        "        if t % 100 == 0:\n",
        "            rand_batch, rand_lab = self.get_random_batch(batch_size)\n",
        "\n",
        "            d_loss = self.sess.run(\n",
        "                self.d_loss, feed_dict={self.target_image: rand_batch, \n",
        "                                        self.target_image_labels: rand_lab,\n",
        "                                        self.synthetic_actions: self.inp_synth_image_acs}\n",
        "            )\n",
        "            g_loss = self.sess.run(\n",
        "                self.g_loss, feed_dict={self.target_image: rand_batch, \n",
        "                                        self.target_image_labels: rand_lab,\n",
        "                                        self.synthetic_actions: self.inp_synth_image_acs}\n",
        "            )\n",
        "            print('Iter [%8d] Time [%5.4f] d_loss [%.4f] g_loss [%.4f]' %\n",
        "                    (t, time.time() - start_time, d_loss, g_loss))\n",
        "\n",
        "  def _init_session(self):\n",
        "    self.sess = tf.Session(graph=self.g)\n",
        "    self.sess.run(self.init)\n",
        "  def close_sess(self):\n",
        "    self.sess.close()\n",
        "    \n",
        "  def load_painter_checkpoint(self, checkpoint_path='tf_conv_vae', actual_path=None):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      if self.painter_type == \"VAE\":\n",
        "        pth = 'conv_vae'\n",
        "      elif self.painter_type == \"GAN\":\n",
        "        pth = 'conv_gan'\n",
        "      saver = tf.train.Saver(tf.global_variables(pth))\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    if actual_path is None:\n",
        "      actual_path = ckpt.model_checkpoint_path\n",
        "    print('loading model', actual_path)\n",
        "    tf.logging.info('Loading model %s.', actual_path)\n",
        "    saver.restore(sess, actual_path)\n",
        "\n",
        "  def save_model(self, model_save_path='adversarial_global_vars'):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    checkpoint_path = os.path.join(model_save_path, 'graph')\n",
        "    tf.logging.info('saving model %s.', checkpoint_path)\n",
        "    saver.save(sess, checkpoint_path, 0) # just keep one\n",
        "  def load_checkpoint(self, checkpoint_path='adversarial_global_vars'):\n",
        "    sess = self.sess\n",
        "    with self.g.as_default():\n",
        "      saver = tf.train.Saver(tf.global_variables())\n",
        "    ckpt = tf.train.get_checkpoint_state(checkpoint_path)\n",
        "    print('loading model', ckpt.model_checkpoint_path)\n",
        "    tf.logging.info('Loading model %s.', ckpt.model_checkpoint_path)\n",
        "    saver.restore(sess, ckpt.model_checkpoint_path)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FOGyccH8T61j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KBKQZero6DfR",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Set up Tensorboard"
      ]
    },
    {
      "metadata": {
        "id": "49mnMb4bWF2N",
        "colab_type": "code",
        "outputId": "7e344889-322c-4c19-bff0-16a3920ada60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        }
      },
      "cell_type": "code",
      "source": [
        "!rm -r logdir\n",
        "\n",
        "\n",
        "get_ipython().system_raw(\n",
        "    'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &'\n",
        "    .format('logdir')\n",
        ")\n",
        "\n",
        "get_ipython().system_raw('./ngrok http 6006 &')\n",
        "\n",
        "!curl -s http://localhost:4040/api/tunnels | python3 -c \\\n",
        "    \"import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])\"\n"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "rm: cannot remove 'logdir': No such file or directory\n",
            "http://39963f1e.ngrok.io\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "ZlRGho_g6IiE",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Actual training"
      ]
    },
    {
      "metadata": {
        "id": "_najWrubN4Cc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "transposed_actions = np.transpose(_input_actions, axes=[1, 0, 2])\n",
        "lol = AdversarialGraph(transposed_actions, num_unique_labels=10, max_seq_len=8, connected=True, painter_type='VAE')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "1bEMna7U6STq",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 145
        },
        "outputId": "f433a6a3-d126-47d9-d6f9-51da7fb814ec"
      },
      "cell_type": "code",
      "source": [
        "# Load GAN painter checkpoint\n",
        "#lol.load_painter_checkpoint('tf_gan3')\n",
        "\n",
        "# Load VAE painter checkpoint\n",
        "lol.load_painter_checkpoint('tf_vae')"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "('loading model', u'tf_vae/vae-300000')\n",
            "INFO:tensorflow:Loading model tf_vae/vae-300000.\n",
            "WARNING:tensorflow:From /usr/local/lib/python2.7/dist-packages/tensorflow/python/training/saver.py:1266: checkpoint_exists (from tensorflow.python.training.checkpoint_management) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use standard file APIs to check for files with this prefix.\n",
            "INFO:tensorflow:Restoring parameters from tf_vae/vae-300000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "NX2bAl0h6WSP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Resume training from checkpoint\n",
        "#lol.load_checkpoint('adversarial_global_vars')"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "G518ssqYLh8D",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#lol.load_checkpoint('adversarial_global_vars')\n",
        "last_arr = [None, None, None]\n",
        "lol.train(last_actions_arr = last_arr, start_batch=0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "S1CtQDpUpr88",
        "colab_type": "code",
        "outputId": "c547ab98-d70b-4bca-c28a-481c3568af56",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "lol.save_model()"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:saving model adversarial_global_vars/graph.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "Zi5PVNkjT4Ko",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "il9R-sg9e_ui",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# evaluate model"
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "4cYl4noTGgv-",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def plot_images(images):\n",
        "  h=args.screen_size\n",
        "  fig=plt.figure(figsize=(16, 16))\n",
        "  columns = len(images)\n",
        "  rows = 1\n",
        "\n",
        "  for i, img in enumerate(images):\n",
        "    img = img[:, :, :3]\n",
        "    #print(img.shape)\n",
        "    fig.add_subplot(rows, columns, i+1)\n",
        "    plt.grid(False)\n",
        "    plt.imshow(img)\n",
        "  plt.show()\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "colab_type": "code",
        "id": "KPsvuD2fGgeK",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "2FhXOE7beodx",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_realEnv = ColorEnv(args, paint_mode=PaintMode.CONNECTED_STROKES)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "NGE8ysZvhIoP",
        "colab_type": "code",
        "outputId": "d789f7d3-ab11-461c-de92-06cb8352a497",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        }
      },
      "cell_type": "code",
      "source": [
        "def run_actions_on_real_env(realEnv, actions_array):\n",
        "  images = []\n",
        "  realEnv.reset()\n",
        "  for ac in actions_array:\n",
        "    print(ac)\n",
        "    realEnv.draw(ac)\n",
        "    images.append(realEnv.image)\n",
        "  plot_images(images)\n",
        "run_actions_on_real_env(_realEnv, last_arr[0][:, 41, :])\n",
        "plot_images(last_arr[1][40:48])\n",
        "plot_images(last_arr[2][40:48])"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.54805005 0.58163255 0.38804722 0.35186815 0.41127133 0.34334347\n",
            " 0.         0.         0.         0.42802572 0.30174035 0.        ]\n",
            "[0.32760412 0.1678591  0.35500312 0.4511524  0.3884344  0.3251331\n",
            " 0.         0.         0.         0.00689456 0.00650376 0.        ]\n",
            "[3.0756778e-01 4.4574517e-01 2.9362535e-01 5.7695484e-01 1.9856933e-01\n",
            " 5.6902575e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.4735162e-03\n",
            " 1.6881526e-03 2.0861626e-07]\n",
            "[0.38924292 0.5073548  0.6420258  0.53072774 0.5931946  0.55163836\n",
            " 0.         0.         0.         0.00618327 0.00331134 0.        ]\n",
            "[3.1135738e-02 2.9745311e-02 5.9248459e-01 2.5749734e-01 6.0506719e-01\n",
            " 2.2102463e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 7.8845024e-04\n",
            " 4.0516257e-04 0.0000000e+00]\n",
            "[0.33347875 0.40435898 0.601946   0.3474545  0.6478757  0.25271422\n",
            " 0.         0.         0.         0.00298709 0.00165895 0.        ]\n",
            "[0.3394868  0.49051374 0.61742896 0.55358005 0.64296156 0.59248024\n",
            " 0.         0.         0.         0.00155547 0.00132447 0.        ]\n",
            "[0.3684328  0.4300555  0.61781335 0.79603803 0.6532771  0.8308438\n",
            " 0.         0.         0.         0.00159875 0.00281644 0.        ]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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d4P9aax8EHgEuAC8Cr1lr9wOvFb6WeFOOflCOflCOflCOflCOflCOflCOW8R9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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x1152 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wogC+Ngzj/wbwHwH4f0zT/CeGYfw5gD8H8I+dfrFcIM7VFva5ccMcSt1uNy+o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\nwSV1mrSzbAAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x1152 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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oDMSOR4i9qzI+JOjdp1SGZ3V1lcXbPT09uHHjBhvjDA4OMkrh9/sZmdDPQCsr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\nRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1152x1152 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "ByxpSrn89dBK",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "LXjtK6WOOGZv",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "KdWQLpFGOVyA",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# Generate GIFs"
      ]
    },
    {
      "metadata": {
        "id": "D-tX46UpR2Dm",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "L1EskcrUOGm3",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "i5j5YmMWSHmP",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_realEnv = ColorEnv(args, paint_mode=PaintMode.CONNECTED_STROKES)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ImHyisDwwELG",
        "colab_type": "code",
        "outputId": "1920d800-2379-4a5f-cb03-c96fc779da45",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 89
        }
      },
      "cell_type": "code",
      "source": [
        "_ran_batch, _ = lol.get_random_batch(20)\n",
        "_ran_batch.shape\n",
        "plot_images(_ran_batch)"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LHRYIgkB9fT25ubls3rxZCl9MSEhgxYoVJCUlWUQXDLwQR9/8AUuP7fMRT9z9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\nEARBEARBELTy/43/Tn6roOldAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1152x1152 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "IwweoPMIOGXz",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_last_actions, _fcstate, _int_canvases = lol.sess.run((lol.last_actions, lol.final_canvas_state, lol.intermediate_canvases), feed_dict={lol.target_image: _ran_batch})\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mEDF6zMfOGV5",
        "colab_type": "code",
        "outputId": "5fc25887-4715-4876-a39f-3d85ed8a4cdc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "_fcstate.shape"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(20, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "metadata": {
        "id": "DnFZR29lOGUD",
        "colab_type": "code",
        "outputId": "e28e9b9e-b46e-4e44-8b35-4a9d31a0e66e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "_last_actions.shape"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 20, 12)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "metadata": {
        "id": "kc7sr8ueSVOw",
        "colab_type": "code",
        "outputId": "0cf8f27e-3f59-437a-a337-92b92096da9d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "_int_canvases.shape"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(8, 20, 64, 64, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "metadata": {
        "id": "4aZ06qxwOGRQ",
        "colab_type": "code",
        "outputId": "b3c649e4-6168-469e-ce47-d625b4c338fd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 873
        }
      },
      "cell_type": "code",
      "source": [
        "def run_actions_on_real_env(realEnv, actions_array):\n",
        "  images = []\n",
        "  realEnv.reset()\n",
        "  for ac in actions_array:\n",
        "    #ColorEnv.pretty_print_action(ac)\n",
        "    #print('___')\n",
        "    print(ac)\n",
        "    realEnv.draw(ac)\n",
        "    images.append(realEnv.image)\n",
        "  return images\n",
        "_imgs = run_actions_on_real_env(_realEnv, _last_actions[:, 0, :])\n",
        "plot_images(_imgs)\n",
        "plot_images(_int_canvases[:, 0, :, :])\n",
        "plot_images(_fcstate[:10])\n",
        "plot_images(_ran_batch[:10])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.5097132  0.59486556 0.53018945 0.29525772 0.57964194 0.30115092\n",
            " 0.         0.         0.         0.47434267 0.3023697  0.        ]\n",
            "[0.25738582 0.14262456 0.5363103  0.27585602 0.52124745 0.25834784\n",
            " 0.         0.         0.         0.00496247 0.00267321 0.        ]\n",
            "[0.36751404 0.3788039  0.59781015 0.2546385  0.6520468  0.26521552\n",
            " 0.         0.         0.         0.00153801 0.00142843 0.        ]\n",
            "[4.1437510e-01 4.1350281e-01 7.3747438e-01 2.8756189e-01 5.3635335e-01\n",
            " 3.9536247e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.2197108e-04\n",
            " 6.0871243e-04 0.0000000e+00]\n",
            "[2.8688806e-01 3.8073674e-01 5.1623112e-01 3.7500757e-01 4.1244084e-01\n",
            " 4.4369739e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.8935075e-04\n",
            " 5.5736303e-04 0.0000000e+00]\n",
            "[3.7230679e-01 4.3000698e-01 6.0393924e-01 3.9846092e-01 7.4606776e-01\n",
            " 5.1594561e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.3772330e-04\n",
            " 1.2061894e-03 0.0000000e+00]\n",
            "[3.9608562e-01 4.6468797e-01 6.5188205e-01 5.9182703e-01 6.6139925e-01\n",
            " 6.8631792e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.6148896e-04\n",
            " 1.0130107e-03 0.0000000e+00]\n",
            "[4.6176746e-01 4.1086867e-01 3.7911755e-01 8.0198848e-01 1.6661677e-01\n",
            " 6.1564088e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.7253242e-04\n",
            " 1.7061830e-03 0.0000000e+00]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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RWXK0IioiIlkViURIJpO8+OKLANy8edPdD7agoMBd5d2zZw/19fVAcJU3n7YT+Sq8Cj/5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\nqzQRFRERERERkazSRFRERERERESy6v8DbzWTMidrwLsAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x1152 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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nf/o6TH9BVnpsgdYaWmuUy2WcnJzg5OQEpVIJ1WoV1WoVWmupehyPx5FIJOQn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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x1152 with 8 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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5JSOSSIyi0Sj29/eZFc/v9w/MGjgWDOKEjgCkX/P5PM+BJt3bzt5oMpng8/mw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\nQUFBQUFBQUHBVvx/fNsTr/ipTOoAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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LNBrF6/XKwYiTk5NMT09TUFBAU1OTHKZ1Vm0lmqZJ/2d7e1veq/X19WRlZZGfny9//mnc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\nAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 1152x1152 with 10 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "colab_type": "code",
        "outputId": "3f0dc1c1-150f-4afb-89e1-cb4e2ffe6fb0",
        "id": "jPdvrrV6UN_C",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 7584
        }
      },
      "cell_type": "code",
      "source": [
        "import imageio\n",
        "\n",
        "_final_to_plot = []\n",
        "for _target in range(20):\n",
        "  run_actions_on_real_env(_realEnv, _last_actions[:, _target, :])\n",
        "  print(len(_realEnv.intermediate_images))\n",
        "  _realEnv.intermediate_images = _realEnv.intermediate_images[0::21]\n",
        "\n",
        "  _inter_images = np.stack(_realEnv.intermediate_images)[:, :, :, :3].astype(np.float)/255.\n",
        "  _target_images = np.tile(_ran_batch[_target].reshape(1, 64, 64, 3), [len(_realEnv.intermediate_images), 1, 1, 1])\n",
        "\n",
        "  _plot = np.concatenate([_target_images, _inter_images], axis=2)\n",
        "  _final_to_plot.append(_plot)\n",
        "\n",
        "_rows = 5\n",
        "_cols = 4\n",
        "_rows_to_stack = []\n",
        "for i in range(_rows):\n",
        "  _rows_to_stack.append(np.concatenate(_final_to_plot[i*_cols:(i+1)*_cols], axis=2))\n",
        "\n",
        "\n",
        "clip = mpy.ImageSequenceClip([x for x in (np.concatenate(_rows_to_stack, axis=1)*255).astype(np.uint8)], fps=14)\n",
        "clip.write_videofile(\"hello.mp4\")\n",
        "display(mpy.ipython_display('hello.mp4', height=200))"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
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            " 6.1607230e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.1466951e-04\n",
            " 1.1788905e-03 0.0000000e+00]\n",
            "[3.5899931e-01 3.8357705e-01 6.4498109e-01 7.1722680e-01 6.4659339e-01\n",
            " 7.5656009e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.9646029e-04\n",
            " 1.0636747e-03 0.0000000e+00]\n",
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            " 6.6359758e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.5089593e-04\n",
            " 1.4384091e-03 0.0000000e+00]\n",
            "824\n",
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            "824\n",
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            " 5.6563443e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.9366741e-03\n",
            " 1.8209815e-03 1.7881393e-07]\n",
            "[0.41574177 0.5299534  0.61975265 0.5524746  0.6270542  0.56307954\n",
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            "[2.6774883e-02 2.9004842e-02 6.4320624e-01 2.4169034e-01 7.2289860e-01\n",
            " 2.0465609e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.3151121e-04\n",
            " 2.7981400e-04 0.0000000e+00]\n",
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            "824\n",
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            " 3.7392735e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.5016069e-04\n",
            " 7.9521537e-04 0.0000000e+00]\n",
            "[4.5281234e-01 5.2685356e-01 4.6972725e-01 2.7437133e-01 5.4677171e-01\n",
            " 2.7672565e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.4524331e-04\n",
            " 6.5422058e-04 0.0000000e+00]\n",
            "[4.4954234e-01 4.6929005e-01 7.0914036e-01 2.9567903e-01 7.5565052e-01\n",
            " 5.3479427e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.1961331e-04\n",
            " 5.4362416e-04 0.0000000e+00]\n",
            "[3.9396623e-01 4.4599202e-01 7.5391674e-01 5.5544662e-01 7.2230721e-01\n",
            " 6.9376701e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.2900443e-04\n",
            " 1.5064180e-03 0.0000000e+00]\n",
            "[3.9450711e-01 3.8053349e-01 6.2185264e-01 7.7180278e-01 4.7403207e-01\n",
            " 8.4210068e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.2747130e-04\n",
            " 1.7333329e-03 0.0000000e+00]\n",
            "824\n",
            "[4.8204088e-01 6.2348491e-01 3.6922446e-01 2.1521711e-01 4.2353785e-01\n",
            " 2.2742364e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.4998703e-01\n",
            " 2.5605270e-01 8.9406967e-08]\n",
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            "824\n",
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            " 4.2826554e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.1870942e-04\n",
            " 5.9676170e-04 0.0000000e+00]\n",
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            "[5.7797599e-01 5.4443073e-01 4.7261339e-01 6.4504278e-01 8.1547737e-01\n",
            " 6.9774127e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.5612793e-04\n",
            " 1.7217994e-03 0.0000000e+00]\n",
            "824\n",
            "[0.41775066 0.6097051  0.38619837 0.31648475 0.49155933 0.1141758\n",
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            "[3.0043435e-01 2.1471402e-01 3.0409712e-01 4.0164834e-01 3.8055649e-01\n",
            " 2.8353369e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.0701895e-02\n",
            " 6.4181685e-03 2.9802322e-08]\n",
            "[3.3395898e-01 3.7322366e-01 3.3293501e-01 4.4769841e-01 3.4802857e-01\n",
            " 5.5198318e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.8669407e-03\n",
            " 2.3807585e-03 5.9604645e-08]\n",
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            "824\n",
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            "[2.6497671e-01 2.9058111e-01 3.1665117e-01 4.7747263e-01 2.5430173e-01\n",
            " 5.4907370e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.3716221e-03\n",
            " 1.1390150e-03 2.0861626e-07]\n",
            "[0.39802223 0.47793025 0.5550483  0.6323243  0.5913047  0.5299197\n",
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            "[5.2637577e-02 3.6147416e-02 5.3928453e-01 2.9453042e-01 5.8942133e-01\n",
            " 2.2936964e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.4025650e-04\n",
            " 3.1182170e-04 0.0000000e+00]\n",
            "[0.32270744 0.3941815  0.57471263 0.28637198 0.61413854 0.25307372\n",
            " 0.         0.         0.         0.00164753 0.00125065 0.        ]\n",
            "[0.28838623 0.3977095  0.6056122  0.5363411  0.60600936 0.5484553\n",
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            " 0.         0.         0.         0.00155172 0.00228214 0.        ]\n",
            "824\n",
            "[6.2787610e-01 6.8183476e-01 4.2888722e-01 7.3957098e-01 4.6769050e-01\n",
            " 7.8551662e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.8319003e-01\n",
            " 7.9609597e-01 2.9802322e-08]\n",
            "[0.49421746 0.32818377 0.33044863 0.7282156  0.3444451  0.75530887\n",
            " 0.         0.         0.         0.00412589 0.01296219 0.        ]\n",
            "[0.43700346 0.453133   0.23720437 0.61338365 0.21705502 0.5601738\n",
            " 0.         0.         0.         0.00105354 0.00308788 0.        ]\n",
            "[4.7435716e-01 4.2650783e-01 2.4353167e-01 3.0958515e-01 2.9959926e-01\n",
            " 3.2190144e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.8115511e-04\n",
            " 8.8182092e-04 0.0000000e+00]\n",
            "[4.2508852e-01 5.1697254e-01 4.0065035e-01 2.1684226e-01 5.7026613e-01\n",
            " 2.4005270e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.3972988e-04\n",
            " 5.6871772e-04 0.0000000e+00]\n",
            "[4.7516638e-01 5.3274500e-01 7.0493597e-01 2.8279921e-01 7.8495014e-01\n",
            " 5.0411612e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.4464726e-04\n",
            " 5.2151084e-04 0.0000000e+00]\n",
            "[4.3563595e-01 5.5914038e-01 8.1005281e-01 6.2714279e-01 7.5918514e-01\n",
            " 7.3018998e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.4021010e-04\n",
            " 1.7661452e-03 0.0000000e+00]\n",
            "[4.5276237e-01 4.9707732e-01 6.3480306e-01 7.8795558e-01 4.6223864e-01\n",
            " 7.8965664e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.8645010e-04\n",
            " 1.9542277e-03 0.0000000e+00]\n",
            "824\n",
            "[MoviePy] >>>> Building video hello.mp4\n",
            "[MoviePy] Writing video hello.mp4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 41/41 [00:00<00:00, 346.17it/s]\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: hello.mp4 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<moviepy.video.io.html_tools.HTML2 object>"
            ],
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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "6DvNirCyTWNs",
        "colab_type": "code",
        "outputId": "ca20fc2b-6f6f-4337-8710-e61cd97d11c5",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 8304
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "_stacked_plots = []\n",
        "for _target in range(0, 20):\n",
        "  run_actions_on_real_env(_realEnv, _last_actions[:, _target, :])\n",
        "  print(len(_realEnv.intermediate_images))\n",
        "  _realEnv.intermediate_images = _realEnv.intermediate_images[0::21]\n",
        "\n",
        "  _inter_images = np.stack(_realEnv.intermediate_images)[:, :, :, :3].astype(np.float)/255.\n",
        "  _intermediate_canvases_to_plot = np.repeat(_int_canvases[:, _target, :, :, :], 90, axis=0)[0::18]\n",
        "  _target_images = np.tile(_ran_batch[_target].reshape(1, 64, 64, 3), [len(_realEnv.intermediate_images), 1, 1, 1])\n",
        "\n",
        "  print(len(_realEnv.intermediate_images))\n",
        "  print(_target_images.shape, _intermediate_canvases_to_plot.shape, _inter_images.shape)\n",
        "\n",
        "  _plot = np.concatenate([_target_images, _intermediate_canvases_to_plot, _inter_images], axis=2)\n",
        "  _stacked_plots.append(_plot)\n",
        "\n",
        "  \n",
        "#imageio.mimsave('hello2.gif', np.concatenate(_stacked_plots),'GIF', fps=14)\n",
        "import moviepy.editor as mpy\n",
        "from IPython.display import display\n",
        "clip = mpy.ImageSequenceClip([x for x in (np.concatenate(_stacked_plots)*255).astype(np.uint8)], fps=14)\n",
        "clip.write_videofile(\"hello2.mp4\")\n",
        "display(mpy.ipython_display('hello2.mp4', height=200))"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.5097132  0.59486556 0.53018945 0.29525772 0.57964194 0.30115092\n",
            " 0.         0.         0.         0.47434267 0.3023697  0.        ]\n",
            "[0.25738582 0.14262456 0.5363103  0.27585602 0.52124745 0.25834784\n",
            " 0.         0.         0.         0.00496247 0.00267321 0.        ]\n",
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            "[4.1437510e-01 4.1350281e-01 7.3747438e-01 2.8756189e-01 5.3635335e-01\n",
            " 3.9536247e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.2197108e-04\n",
            " 6.0871243e-04 0.0000000e+00]\n",
            "[2.8688806e-01 3.8073674e-01 5.1623112e-01 3.7500757e-01 4.1244084e-01\n",
            " 4.4369739e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.8935075e-04\n",
            " 5.5736303e-04 0.0000000e+00]\n",
            "[3.7230679e-01 4.3000698e-01 6.0393924e-01 3.9846092e-01 7.4606776e-01\n",
            " 5.1594561e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.3772330e-04\n",
            " 1.2061894e-03 0.0000000e+00]\n",
            "[3.9608562e-01 4.6468797e-01 6.5188205e-01 5.9182703e-01 6.6139925e-01\n",
            " 6.8631792e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.6148896e-04\n",
            " 1.0130107e-03 0.0000000e+00]\n",
            "[4.6176746e-01 4.1086867e-01 3.7911755e-01 8.0198848e-01 1.6661677e-01\n",
            " 6.1564088e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.7253242e-04\n",
            " 1.7061830e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.4633314  0.72193074 0.4105392  0.38469043 0.43463093 0.29729596\n",
            " 0.         0.         0.         0.4131922  0.36872524 0.        ]\n",
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            " 0.         0.         0.         0.00551587 0.00410965 0.        ]\n",
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            " 0.         0.         0.         0.00227767 0.00208643 0.        ]\n",
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            " 0.         0.         0.         0.00140023 0.00149348 0.        ]\n",
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            " 0.         0.         0.         0.00129855 0.00131586 0.        ]\n",
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            " 0.         0.         0.         0.00092879 0.00233075 0.        ]\n",
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            "[5.4103297e-01 4.9206978e-01 5.0761813e-01 6.8735325e-01 7.5119364e-01\n",
            " 7.1029514e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 7.4014068e-04\n",
            " 2.1748543e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
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            " 0.         0.         0.         0.24005526 0.36056763 0.        ]\n",
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            " 0.         0.         0.         0.00260469 0.00274763 0.        ]\n",
            "[3.7610996e-01 4.0301073e-01 4.6993378e-01 3.1269470e-01 4.7995669e-01\n",
            " 3.1638905e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.7683716e-04\n",
            " 5.5468082e-04 0.0000000e+00]\n",
            "[4.4176045e-01 5.6075495e-01 6.4620876e-01 3.2995176e-01 6.9624436e-01\n",
            " 4.3365860e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.5449862e-04\n",
            " 4.9787760e-04 0.0000000e+00]\n",
            "[4.4106603e-01 5.6734121e-01 6.7301869e-01 5.7273799e-01 5.2775371e-01\n",
            " 6.9433373e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.9353967e-04\n",
            " 4.9328804e-04 0.0000000e+00]\n",
            "[4.1753918e-01 4.6810362e-01 3.9008906e-01 8.5882854e-01 3.4444219e-01\n",
            " 8.7569094e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 7.0601702e-04\n",
            " 1.8359721e-03 0.0000000e+00]\n",
            "[5.5722415e-02 6.9049269e-02 4.8714098e-01 7.2704780e-01 5.2828217e-01\n",
            " 6.6612387e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.2925944e-04\n",
            " 9.9939108e-04 0.0000000e+00]\n",
            "[0.43858594 0.44706255 0.5521035  0.6689713  0.5118839  0.7312468\n",
            " 0.         0.         0.         0.00119647 0.00214282 0.        ]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
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            " 0.         0.         0.         0.00295052 0.00695109 0.        ]\n",
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            " 0.         0.         0.         0.00100923 0.00242275 0.        ]\n",
            "[4.5698661e-01 4.3117714e-01 3.9368504e-01 4.2849499e-01 3.4457019e-01\n",
            " 4.3849158e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.7541986e-04\n",
            " 8.7097287e-04 0.0000000e+00]\n",
            "[4.2995965e-01 5.1227278e-01 5.2443814e-01 2.7996683e-01 5.9503323e-01\n",
            " 2.1643057e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.1790137e-04\n",
            " 7.4562430e-04 0.0000000e+00]\n",
            "[4.6766531e-01 4.7916430e-01 7.1405029e-01 2.4822590e-01 7.2983360e-01\n",
            " 4.6592286e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.6375055e-04\n",
            " 4.5195222e-04 0.0000000e+00]\n",
            "[4.3862519e-01 4.6444601e-01 7.3424292e-01 5.0593531e-01 6.6107166e-01\n",
            " 6.4365017e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.6992302e-04\n",
            " 1.4426708e-03 0.0000000e+00]\n",
            "[4.4093668e-01 4.2511868e-01 6.0696965e-01 6.4148790e-01 4.7440198e-01\n",
            " 7.8215152e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.3751469e-04\n",
            " 1.4901161e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.6870315  0.47148624 0.4832751  0.45869145 0.4773611  0.52702785\n",
            " 0.         0.         0.         0.46546566 0.54631555 0.        ]\n",
            "[2.6802939e-01 3.3034742e-01 5.2160501e-01 4.3308884e-01 5.0142515e-01\n",
            " 4.7163299e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.3758154e-03\n",
            " 2.8491318e-03 2.9802322e-08]\n",
            "[0.33659258 0.46439153 0.69585586 0.22757101 0.61830884 0.29801658\n",
            " 0.         0.         0.         0.00262859 0.002902   0.        ]\n",
            "[0.318727   0.33986974 0.52365357 0.45342714 0.47311175 0.5194238\n",
            " 0.         0.         0.         0.00099674 0.00098255 0.        ]\n",
            "[0.32661307 0.4095991  0.399063   0.6481175  0.35257453 0.72166884\n",
            " 0.         0.         0.         0.00211892 0.00375822 0.        ]\n",
            "[2.1172315e-01 3.5949314e-01 3.3838060e-01 8.0284286e-01 3.3026963e-01\n",
            " 7.5611305e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 7.9804659e-04\n",
            " 2.0695031e-03 0.0000000e+00]\n",
            "[2.2970048e-01 3.1206405e-01 3.8826513e-01 7.1270299e-01 3.3936709e-01\n",
            " 6.8589538e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.8126450e-04\n",
            " 1.7714500e-03 0.0000000e+00]\n",
            "[2.7099749e-01 3.1250009e-01 4.0746754e-01 6.3578051e-01 3.6179775e-01\n",
            " 7.0179451e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.3609738e-04\n",
            " 1.3031363e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.4712808  0.5880701  0.2961042  0.28789005 0.34223145 0.279266\n",
            " 0.         0.         0.         0.2613749  0.29687434 0.        ]\n",
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            " 0.         0.         0.         0.00459331 0.00330168 0.        ]\n",
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            " 0.         0.         0.         0.00106001 0.00082862 0.        ]\n",
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            " 0.         0.         0.         0.00070706 0.00077677 0.        ]\n",
            "[0.39882    0.44844127 0.5505227  0.5586655  0.41902333 0.76470435\n",
            " 0.         0.         0.         0.00097945 0.00127381 0.        ]\n",
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            " 0.         0.         0.         0.00097293 0.00185227 0.        ]\n",
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            " 0.         0.         0.         0.00086677 0.0024341  0.        ]\n",
            "[4.7508815e-01 4.4280148e-01 5.0204414e-01 6.9997120e-01 7.8217703e-01\n",
            " 7.6386476e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.2164664e-04\n",
            " 1.4914274e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.638725   0.71294    0.49152198 0.3785192  0.51102704 0.32874537\n",
            " 0.         0.         0.         0.5222047  0.30002838 0.        ]\n",
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            " 0.         0.         0.         0.00380164 0.00320947 0.        ]\n",
            "[2.9199690e-01 3.6223119e-01 4.5682582e-01 3.9010954e-01 4.4336319e-01\n",
            " 4.2940128e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 9.6303225e-04\n",
            " 8.0934167e-04 2.9802322e-08]\n",
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            " 0.         0.         0.         0.0023891  0.00307155 0.        ]\n",
            "[5.1365364e-01 3.7430680e-01 3.3910942e-01 8.3054733e-01 2.7129769e-01\n",
            " 7.7304721e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.8292665e-04\n",
            " 5.1307678e-04 0.0000000e+00]\n",
            "[4.2476401e-01 5.3403389e-01 4.5597565e-01 5.6821650e-01 6.6124523e-01\n",
            " 4.8929182e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.1085482e-04\n",
            " 5.9312582e-04 0.0000000e+00]\n",
            "[3.9310235e-01 3.9053732e-01 7.0476890e-01 3.3485824e-01 7.6743656e-01\n",
            " 3.1406951e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.5553832e-04\n",
            " 2.5656819e-04 0.0000000e+00]\n",
            "[4.2947242e-01 4.2549869e-01 6.2664354e-01 2.3464027e-01 4.7894523e-01\n",
            " 3.1280601e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.5612454e-04\n",
            " 6.7856908e-04 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.43982622 0.6257223  0.44412512 0.34875128 0.4707903  0.30550134\n",
            " 0.         0.         0.         0.42490283 0.34953868 0.        ]\n",
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            " 0.         0.         0.         0.00681424 0.00341666 0.        ]\n",
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            " 0.         0.         0.         0.00208291 0.00202733 0.        ]\n",
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            " 0.         0.         0.         0.00105432 0.00130945 0.        ]\n",
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            " 0.         0.         0.         0.00117782 0.00174078 0.        ]\n",
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            " 0.         0.         0.         0.00097901 0.00237042 0.        ]\n",
            "[0.3499842  0.468554   0.45527682 0.78042185 0.5113521  0.7228016\n",
            " 0.         0.         0.         0.00087944 0.00238958 0.        ]\n",
            "[4.0278086e-01 3.7018937e-01 4.8126936e-01 7.3989022e-01 7.6805240e-01\n",
            " 7.3992872e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.9198995e-04\n",
            " 1.0518432e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.49884942 0.63301337 0.47841868 0.27715063 0.49133092 0.25627875\n",
            " 0.         0.         0.         0.46164268 0.28407618 0.        ]\n",
            "[0.27339387 0.1629099  0.4194899  0.27091938 0.40662873 0.2669443\n",
            " 0.         0.         0.         0.00767276 0.00414246 0.        ]\n",
            "[0.37563682 0.4672365  0.54479814 0.23268366 0.67168444 0.2680783\n",
            " 0.         0.         0.         0.0020985  0.00185654 0.        ]\n",
            "[4.5987877e-01 4.1202074e-01 6.8708205e-01 3.1411931e-01 5.4849499e-01\n",
            " 4.8345253e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.1613321e-04\n",
            " 8.7043643e-04 0.0000000e+00]\n",
            "[3.3012739e-01 4.2097056e-01 4.2502311e-01 4.6615559e-01 3.3326161e-01\n",
            " 5.0135726e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.9890409e-04\n",
            " 7.6472759e-04 0.0000000e+00]\n",
            "[3.8364428e-01 4.2983124e-01 4.7074911e-01 4.7669852e-01 6.2904823e-01\n",
            " 5.5032307e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.9775839e-04\n",
            " 1.3574660e-03 0.0000000e+00]\n",
            "[4.0459180e-01 4.2453253e-01 6.3255590e-01 6.7019981e-01 6.3894761e-01\n",
            " 7.0640647e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.3783913e-04\n",
            " 1.0257959e-03 0.0000000e+00]\n",
            "[4.8325157e-01 4.7946438e-01 4.1001540e-01 8.1903517e-01 2.0834839e-01\n",
            " 5.9058815e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.3389540e-04\n",
            " 1.6201735e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.51561975 0.541003   0.3970961  0.34101444 0.44140744 0.2579819\n",
            " 0.         0.         0.         0.4467479  0.24714476 0.        ]\n",
            "[3.2376081e-01 1.7183572e-01 3.3869979e-01 4.4892371e-01 3.6291039e-01\n",
            " 3.5886759e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.1840713e-03\n",
            " 3.9869547e-03 2.9802322e-08]\n",
            "[3.2280400e-01 4.0324169e-01 3.1359386e-01 5.6074214e-01 2.0037937e-01\n",
            " 6.5019512e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.7383397e-03\n",
            " 1.7619431e-03 1.7881393e-07]\n",
            "[0.35490865 0.46160603 0.58617973 0.55971575 0.7378744  0.54460156\n",
            " 0.         0.         0.         0.00831071 0.00336045 0.        ]\n",
            "[2.3412555e-02 2.6270628e-02 7.4892449e-01 2.2070295e-01 7.7698791e-01\n",
            " 2.2513252e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.2806735e-04\n",
            " 1.7789006e-04 0.0000000e+00]\n",
            "[0.28660518 0.39907968 0.7332328  0.36483723 0.8106488  0.2746937\n",
            " 0.         0.         0.         0.00156689 0.00141567 0.        ]\n",
            "[0.29965162 0.3724654  0.64635885 0.6051065  0.65879744 0.58969754\n",
            " 0.         0.         0.         0.00154239 0.00158492 0.        ]\n",
            "[0.3066104  0.37277752 0.5938972  0.80779624 0.5948166  0.8350942\n",
            " 0.         0.         0.         0.00161535 0.00319263 0.        ]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.47625357 0.6304114  0.51816857 0.31232738 0.6400884  0.12427548\n",
            " 0.         0.         0.         0.49317285 0.30821046 0.        ]\n",
            "[3.0508834e-01 1.7708650e-01 3.9063293e-01 3.7865543e-01 4.5311642e-01\n",
            " 3.0355474e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 7.7130198e-03\n",
            " 4.5199692e-03 5.9604645e-08]\n",
            "[0.28397948 0.36979347 0.38035893 0.44565862 0.35608855 0.5688399\n",
            " 0.         0.         0.         0.00326329 0.00238201 0.        ]\n",
            "[0.36644584 0.37493455 0.4126006  0.68305147 0.5551225  0.8114198\n",
            " 0.         0.         0.         0.00440216 0.00442228 0.        ]\n",
            "[0.35575485 0.28467262 0.67009073 0.70712095 0.79051745 0.64799976\n",
            " 0.         0.         0.         0.00084656 0.00142109 0.        ]\n",
            "[0.28063136 0.3853882  0.5923669  0.49597198 0.37847582 0.61763126\n",
            " 0.         0.         0.         0.00079846 0.00077391 0.        ]\n",
            "[0.24375468 0.31322485 0.41761616 0.70800817 0.4473216  0.6993966\n",
            " 0.         0.         0.         0.0011757  0.00248593 0.        ]\n",
            "[2.5517374e-01 2.7717918e-01 4.7950119e-01 7.4751264e-01 5.2252179e-01\n",
            " 7.6312935e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.2090158e-04\n",
            " 2.6974678e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.6126134  0.61044073 0.706712   0.26585203 0.7669818  0.28837377\n",
            " 0.         0.         0.         0.69199324 0.260785   0.        ]\n",
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            " 2.5541973e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 8.5273981e-03\n",
            " 4.4603944e-03 8.9406967e-08]\n",
            "[0.34571022 0.43271858 0.4576707  0.24436244 0.43582356 0.27333677\n",
            " 0.         0.         0.         0.00143588 0.00122806 0.        ]\n",
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            " 0.         0.         0.         0.00094756 0.00121504 0.        ]\n",
            "[3.4114718e-01 3.7400848e-01 4.2367381e-01 4.3830773e-01 3.9209557e-01\n",
            " 5.3606731e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.0944090e-04\n",
            " 7.9408288e-04 0.0000000e+00]\n",
            "[3.6191136e-01 3.4220922e-01 5.3101879e-01 5.2879906e-01 6.7181367e-01\n",
            " 6.1607230e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.1466951e-04\n",
            " 1.1788905e-03 0.0000000e+00]\n",
            "[3.5899931e-01 3.8357705e-01 6.4498109e-01 7.1722680e-01 6.4659339e-01\n",
            " 7.5656009e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.9646029e-04\n",
            " 1.0636747e-03 0.0000000e+00]\n",
            "[4.0215746e-01 3.7302878e-01 4.2924690e-01 8.6340332e-01 2.6486710e-01\n",
            " 6.6359758e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.5089593e-04\n",
            " 1.4384091e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.5281838  0.6059573  0.5298235  0.4760748  0.5362755  0.44602394\n",
            " 0.         0.         0.         0.50773275 0.46995288 0.        ]\n",
            "[0.22021383 0.25524575 0.54145586 0.3953739  0.5472789  0.3723781\n",
            " 0.         0.         0.         0.00337484 0.00200006 0.        ]\n",
            "[0.27517176 0.3940828  0.64070123 0.2569089  0.6028639  0.31428146\n",
            " 0.         0.         0.         0.00308493 0.00318441 0.        ]\n",
            "[0.2579176  0.26110205 0.49740562 0.6011669  0.50838125 0.5033772\n",
            " 0.         0.         0.         0.00135592 0.00141761 0.        ]\n",
            "[0.23366174 0.3499894  0.4397267  0.7382792  0.44168943 0.7174746\n",
            " 0.         0.         0.         0.00206196 0.00320137 0.        ]\n",
            "[0.15286931 0.2759491  0.44552776 0.86566854 0.46300888 0.7561876\n",
            " 0.         0.         0.         0.00126413 0.00262758 0.        ]\n",
            "[0.15780029 0.26559383 0.45266533 0.827986   0.4383217  0.7005351\n",
            " 0.         0.         0.         0.00093725 0.00265935 0.        ]\n",
            "[0.202324   0.29082343 0.47042993 0.8088628  0.44714513 0.7315304\n",
            " 0.         0.         0.         0.00088409 0.00265625 0.        ]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.492631   0.5478706  0.3536663  0.377272   0.40757754 0.29820997\n",
            " 0.         0.         0.         0.42902535 0.29925087 0.        ]\n",
            "[0.32402492 0.1920509  0.35711104 0.4717066  0.40216228 0.33853394\n",
            " 0.         0.         0.         0.00858012 0.00640827 0.        ]\n",
            "[3.2276595e-01 4.1001627e-01 3.2858399e-01 5.4374754e-01 2.5775963e-01\n",
            " 5.6563443e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.9366741e-03\n",
            " 1.8209815e-03 1.7881393e-07]\n",
            "[0.41574177 0.5299534  0.61975265 0.5524746  0.6270542  0.56307954\n",
            " 0.         0.         0.         0.00857967 0.00403753 0.        ]\n",
            "[2.6774883e-02 2.9004842e-02 6.4320624e-01 2.4169034e-01 7.2289860e-01\n",
            " 2.0465609e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.3151121e-04\n",
            " 2.7981400e-04 0.0000000e+00]\n",
            "[0.30915886 0.41471875 0.63737965 0.30087414 0.66162694 0.27233148\n",
            " 0.         0.         0.         0.00241917 0.00139004 0.        ]\n",
            "[0.34203875 0.45801222 0.58890486 0.5723275  0.60379976 0.54978836\n",
            " 0.         0.         0.         0.00194719 0.00169113 0.        ]\n",
            "[0.38103735 0.4489976  0.5503003  0.75948775 0.5552261  0.7736594\n",
            " 0.         0.         0.         0.00180161 0.00295547 0.        ]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.60889363 0.6687703  0.42394084 0.8411904  0.39249855 0.86587405\n",
            " 0.         0.         0.         0.4774226  0.8248274  0.        ]\n",
            "[0.43081748 0.2969362  0.30881166 0.79262817 0.25487918 0.7894181\n",
            " 0.         0.         0.         0.00290188 0.00866032 0.        ]\n",
            "[0.41920263 0.3628348  0.25009686 0.6496024  0.22287923 0.5575028\n",
            " 0.         0.         0.         0.00081104 0.00246513 0.        ]\n",
            "[4.6420661e-01 3.7984857e-01 3.1562394e-01 3.8348553e-01 3.1163394e-01\n",
            " 3.7392735e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.5016069e-04\n",
            " 7.9521537e-04 0.0000000e+00]\n",
            "[4.5281234e-01 5.2685356e-01 4.6972725e-01 2.7437133e-01 5.4677171e-01\n",
            " 2.7672565e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.4524331e-04\n",
            " 6.5422058e-04 0.0000000e+00]\n",
            "[4.4954234e-01 4.6929005e-01 7.0914036e-01 2.9567903e-01 7.5565052e-01\n",
            " 5.3479427e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.1961331e-04\n",
            " 5.4362416e-04 0.0000000e+00]\n",
            "[3.9396623e-01 4.4599202e-01 7.5391674e-01 5.5544662e-01 7.2230721e-01\n",
            " 6.9376701e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.2900443e-04\n",
            " 1.5064180e-03 0.0000000e+00]\n",
            "[3.9450711e-01 3.8053349e-01 6.2185264e-01 7.7180278e-01 4.7403207e-01\n",
            " 8.4210068e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.2747130e-04\n",
            " 1.7333329e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[4.8204088e-01 6.2348491e-01 3.6922446e-01 2.1521711e-01 4.2353785e-01\n",
            " 2.2742364e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.4998703e-01\n",
            " 2.5605270e-01 8.9406967e-08]\n",
            "[0.33541876 0.16560134 0.40420926 0.22560284 0.3715503  0.25568593\n",
            " 0.         0.         0.         0.00915962 0.00452381 0.        ]\n",
            "[0.3729056  0.38315484 0.524599   0.17560917 0.60314757 0.2761998\n",
            " 0.         0.         0.         0.00305012 0.00192714 0.        ]\n",
            "[0.36941797 0.36287504 0.57366943 0.30159894 0.5545064  0.45032597\n",
            " 0.         0.         0.         0.00122228 0.00101474 0.        ]\n",
            "[0.37806225 0.4500526  0.5221246  0.52719396 0.42365363 0.70840937\n",
            " 0.         0.         0.         0.00204241 0.00167087 0.        ]\n",
            "[0.32983345 0.36305058 0.30341446 0.7527605  0.24682239 0.78773427\n",
            " 0.         0.         0.         0.00155953 0.00415757 0.        ]\n",
            "[0.40983683 0.40063596 0.318914   0.67503107 0.3409292  0.68596673\n",
            " 0.         0.         0.         0.00107613 0.00313404 0.        ]\n",
            "[5.2414811e-01 4.5289421e-01 4.8558941e-01 6.0323286e-01 8.0640268e-01\n",
            " 7.6593590e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.3636899e-04\n",
            " 1.7681718e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.5494103  0.6348427  0.40559357 0.3218095  0.37775207 0.30555338\n",
            " 0.         0.         0.         0.36491334 0.35115817 0.        ]\n",
            "[0.35365343 0.18339399 0.43544915 0.25784743 0.3665195  0.3013919\n",
            " 0.         0.         0.         0.0061639  0.0038698  0.        ]\n",
            "[0.41898108 0.45468265 0.5491891  0.1829128  0.6547924  0.28034574\n",
            " 0.         0.         0.         0.00163478 0.00110951 0.        ]\n",
            "[4.1986376e-01 4.2086327e-01 6.4495927e-01 2.7380204e-01 6.3552356e-01\n",
            " 4.2826554e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.1870942e-04\n",
            " 5.9676170e-04 0.0000000e+00]\n",
            "[0.44130608 0.5224754  0.58919793 0.48804918 0.48370445 0.6820653\n",
            " 0.         0.         0.         0.00072631 0.0009768  0.        ]\n",
            "[0.41062844 0.45128834 0.3434125  0.72595114 0.2222201  0.76834047\n",
            " 0.         0.         0.         0.00126633 0.00397199 0.        ]\n",
            "[0.4461569  0.49499238 0.26964167 0.67742    0.28498387 0.6595955\n",
            " 0.         0.         0.         0.00097841 0.00236815 0.        ]\n",
            "[5.7797599e-01 5.4443073e-01 4.7261339e-01 6.4504278e-01 8.1547737e-01\n",
            " 6.9774127e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 6.5612793e-04\n",
            " 1.7217994e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.41775066 0.6097051  0.38619837 0.31648475 0.49155933 0.1141758\n",
            " 0.         0.         0.         0.4552601  0.27787882 0.        ]\n",
            "[3.0043435e-01 2.1471402e-01 3.0409712e-01 4.0164834e-01 3.8055649e-01\n",
            " 2.8353369e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.0701895e-02\n",
            " 6.4181685e-03 2.9802322e-08]\n",
            "[3.3395898e-01 3.7322366e-01 3.3293501e-01 4.4769841e-01 3.4802857e-01\n",
            " 5.5198318e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 3.8669407e-03\n",
            " 2.3807585e-03 5.9604645e-08]\n",
            "[0.35638955 0.40332183 0.38176066 0.64934325 0.58416456 0.73765844\n",
            " 0.         0.         0.         0.00296074 0.00236079 0.        ]\n",
            "[0.42038015 0.34664655 0.6912187  0.7164459  0.8461988  0.63619673\n",
            " 0.         0.         0.         0.00101504 0.00155076 0.        ]\n",
            "[0.2812029  0.41539246 0.6189499  0.44607934 0.3689425  0.6108121\n",
            " 0.         0.         0.         0.00081024 0.00066927 0.        ]\n",
            "[0.25049597 0.32466316 0.4229499  0.702994   0.44564646 0.680346\n",
            " 0.         0.         0.         0.00128263 0.00202355 0.        ]\n",
            "[0.26138252 0.29508388 0.53302616 0.73652    0.62845206 0.75106514\n",
            " 0.         0.         0.         0.00100014 0.00334421 0.        ]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[0.48911828 0.5747864  0.27675283 0.36144534 0.31724393 0.3178205\n",
            " 0.         0.         0.         0.37334377 0.2581923  0.        ]\n",
            "[0.36101526 0.11401066 0.2773052  0.42965004 0.32705623 0.3138275\n",
            " 0.         0.         0.         0.00461632 0.00418612 0.        ]\n",
            "[2.6497671e-01 2.9058111e-01 3.1665117e-01 4.7747263e-01 2.5430173e-01\n",
            " 5.4907370e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 1.3716221e-03\n",
            " 1.1390150e-03 2.0861626e-07]\n",
            "[0.39802223 0.47793025 0.5550483  0.6323243  0.5913047  0.5299197\n",
            " 0.         0.         0.         0.00531337 0.00295553 0.        ]\n",
            "[5.2637577e-02 3.6147416e-02 5.3928453e-01 2.9453042e-01 5.8942133e-01\n",
            " 2.2936964e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.4025650e-04\n",
            " 3.1182170e-04 0.0000000e+00]\n",
            "[0.32270744 0.3941815  0.57471263 0.28637198 0.61413854 0.25307372\n",
            " 0.         0.         0.         0.00164753 0.00125065 0.        ]\n",
            "[0.28838623 0.3977095  0.6056122  0.5363411  0.60600936 0.5484553\n",
            " 0.         0.         0.         0.00106266 0.00089121 0.        ]\n",
            "[0.34020078 0.4119081  0.6839912  0.7630894  0.67794347 0.7894449\n",
            " 0.         0.         0.         0.00155172 0.00228214 0.        ]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[6.2787610e-01 6.8183476e-01 4.2888722e-01 7.3957098e-01 4.6769050e-01\n",
            " 7.8551662e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.8319003e-01\n",
            " 7.9609597e-01 2.9802322e-08]\n",
            "[0.49421746 0.32818377 0.33044863 0.7282156  0.3444451  0.75530887\n",
            " 0.         0.         0.         0.00412589 0.01296219 0.        ]\n",
            "[0.43700346 0.453133   0.23720437 0.61338365 0.21705502 0.5601738\n",
            " 0.         0.         0.         0.00105354 0.00308788 0.        ]\n",
            "[4.7435716e-01 4.2650783e-01 2.4353167e-01 3.0958515e-01 2.9959926e-01\n",
            " 3.2190144e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.8115511e-04\n",
            " 8.8182092e-04 0.0000000e+00]\n",
            "[4.2508852e-01 5.1697254e-01 4.0065035e-01 2.1684226e-01 5.7026613e-01\n",
            " 2.4005270e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.3972988e-04\n",
            " 5.6871772e-04 0.0000000e+00]\n",
            "[4.7516638e-01 5.3274500e-01 7.0493597e-01 2.8279921e-01 7.8495014e-01\n",
            " 5.0411612e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 2.4464726e-04\n",
            " 5.2151084e-04 0.0000000e+00]\n",
            "[4.3563595e-01 5.5914038e-01 8.1005281e-01 6.2714279e-01 7.5918514e-01\n",
            " 7.3018998e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 4.4021010e-04\n",
            " 1.7661452e-03 0.0000000e+00]\n",
            "[4.5276237e-01 4.9707732e-01 6.3480306e-01 7.8795558e-01 4.6223864e-01\n",
            " 7.8965664e-01 0.0000000e+00 0.0000000e+00 0.0000000e+00 5.8645010e-04\n",
            " 1.9542277e-03 0.0000000e+00]\n",
            "824\n",
            "40\n",
            "((40, 64, 64, 3), (40, 64, 64, 3), (40, 64, 64, 3))\n",
            "[MoviePy] >>>> Building video hello2.mp4\n",
            "[MoviePy] Writing video hello2.mp4\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "100%|██████████| 800/800 [00:00<00:00, 1547.88it/s]"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[MoviePy] Done.\n",
            "[MoviePy] >>>> Video ready: hello2.mp4 \n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<moviepy.video.io.html_tools.HTML2 object>"
            ],
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controls>Sorry, seems like your browser doesn't support HTML5 audio/video</video></div>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "zs3bfiUQQG2_",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "0cwY41zbQG0j",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "rAWjrMDqQGx2",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "04T_2GXfuY1o",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}